[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-ZHOUYI1023--awesome-radar-perception":3,"tool-ZHOUYI1023--awesome-radar-perception":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",150037,2,"2026-04-10T23:33:47",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":77,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":77,"owner_url":80,"languages":77,"stars":81,"forks":82,"last_commit_at":83,"license":77,"difficulty_score":84,"env_os":85,"env_gpu":86,"env_ram":86,"env_deps":87,"category_tags":90,"github_topics":91,"view_count":32,"oss_zip_url":77,"oss_zip_packed_at":77,"status":17,"created_at":100,"updated_at":101,"faqs":102,"releases":103},6494,"ZHOUYI1023\u002Fawesome-radar-perception","awesome-radar-perception"," A curated list of radar datasets, detection, tracking and fusion","awesome-radar-perception 是一个专注于雷达感知领域的开源资源合集，旨在为自动驾驶及相关技术的研究提供一站式支持。它系统性地整理了雷达数据集、检测算法、目标跟踪、多传感器融合等关键内容，并持续更新前沿成果。\n\n在自动驾驶研发中，雷达数据往往分散且处理复杂，开发者难以快速找到高质量的基准数据或成熟算法。awesome-radar-perception 通过分类汇总主流数据集（如 nuScenes、Radar Scenes 等）、信号处理工具包以及针对天气干扰、多径效应等挑战的解决方案，有效降低了研究门槛，帮助用户高效获取所需资源。\n\n该资源库特别适合从事自动驾驶感知算法的研究人员、工程师以及高校师生使用。无论是需要训练数据的算法开发者，还是希望了解雷达最新进展的学术研究者，都能从中获益。其独特亮点在于不仅涵盖了从数据标注、仿真生成到深度学习应用的全流程资源，还关联了作者发表的综述论文与详细课件，为深入理解“深度雷达感知”提供了坚实的理论支撑与实践指引。作为一个由社区维护的动态知识库，awesome-radar-perception 正成为连接雷达技术与智能驾驶应用的","awesome-radar-perception 是一个专注于雷达感知领域的开源资源合集，旨在为自动驾驶及相关技术的研究提供一站式支持。它系统性地整理了雷达数据集、检测算法、目标跟踪、多传感器融合等关键内容，并持续更新前沿成果。\n\n在自动驾驶研发中，雷达数据往往分散且处理复杂，开发者难以快速找到高质量的基准数据或成熟算法。awesome-radar-perception 通过分类汇总主流数据集（如 nuScenes、Radar Scenes 等）、信号处理工具包以及针对天气干扰、多径效应等挑战的解决方案，有效降低了研究门槛，帮助用户高效获取所需资源。\n\n该资源库特别适合从事自动驾驶感知算法的研究人员、工程师以及高校师生使用。无论是需要训练数据的算法开发者，还是希望了解雷达最新进展的学术研究者，都能从中获益。其独特亮点在于不仅涵盖了从数据标注、仿真生成到深度学习应用的全流程资源，还关联了作者发表的综述论文与详细课件，为深入理解“深度雷达感知”提供了坚实的理论支撑与实践指引。作为一个由社区维护的动态知识库，awesome-radar-perception 正成为连接雷达技术与智能驾驶应用的重要桥梁。","\u003Cdiv align=\"center\">\n    \u003Cimg class=\"aligncenter\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FZHOUYI1023_awesome-radar-perception_readme_8ddb0a3a085d.png\"\u002F>\n\u003C\u002Fdiv>\n\u003Cbr>\n\n\nA curated list of radar datasets, detection, tracking and fusion. \u003Cbr>Keep updating.\u003Cbr>Author: Yi Zhou\u003Cbr>Contact: zhouyi1023@tju.edu.cn\n\n\n🚩I have published a review paper on radar perception. Please see the link below. It is open access. If you find the contents are useful, please cite this paper in your work. I will keep updating this repository for the latest works in the radar perception field.\n\n## [Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F22\u002F11\u002F4208)\n## The 41-page slides associated with this paper: [Link](https:\u002F\u002Fwww.slideshare.net\u002FYiZhou66\u002Fslidesdeepradarperceptionforautonomousdrivingpdf) ; [Link for China Mainland](https:\u002F\u002Fwww.aliyundrive.com\u002Fs\u002FCZ3SKqY3U4w) \n\n---\n\n## Contents\nOverview\n- [Review](#Review-Papers)\n- [Seminars and Workshops](#Seminars-and-Workshops)\n\nData Perspective:\n- [Radar Datasets](#Radar-Datasets)\n- [Radar Signature](#Radar-Signature)\n- [Calibration](#Calibration)\n- [Labelling](#Labelling)\n- [Augmentation](#Data-Augmentation)\n- [Simulation](#Simulator)\n- [Generative Model](#Generative-Model)\n- [Testing](#Testing)\n\nSignal Processing:\n- [Radar Toolbox](#Radar-Toolbox)\n- [MIMO Calibration](#MIMO-Calibration)\n- [Detector](#Detector)\n- [Super Resolution](#Super-Resolution)\n- [Clustering](#Clustering)\n- [Denoising](#Denoising)\n\n\nApplications:\n- [TI Reference Designs](#TI-Reference-Designs)\n- [Ego-Motion Estimation](#Ego-Motion-Estimation)\n- [Velocity Estimation](#Velocity-Estimation)\n- [Depth Estimation](#Depth-Estimation)\n- [Object Detection](#Object-Detection)\n- [Sensor Fusion](#Sensor-Fusion)\n- [Weakly Supervised](#Weakly-Supervised)\n- [Tracking](#Tracking)\n- [Prediction](#Prediction)\n- [Occupancy Grid Map](#Occupancy-Grid-Map)\n- [Open Space Segmentation](#Open-Space-Segmentation)\n- [Scene Understanding (Static Segmentation)](#Scene-Understanding)\n- [Place Recognition](#Place-Recognition)\n- [Odometry and SLAM](#Odometry-and-SLAM)\n- [Automotive SAR](#Automotive-SAR)\n- [Human Activity](#Human-Activity-Recognition)\n- [Radar-Audio](#Radar-Audio)\n\nChallenges:\n- [Weather Effect](#Weather-Effects)\n- [Multi Path Effect](#Multi-Path-Effect)\n- [Mutual Interference](#Mutual-Interference)\n- [Cell Migration](#Range-and-Doppler-Cell-Migration)\n- [Tx-Rx Leakage](#Tx-Rx-Leakage)\n- [Imperfect Waveform Separation](#Imperfect-Waveform-Separation)\n\u003Cbr>\n\n---\n\n## Radar Datasets\nIn my [review paper](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F22\u002F11\u002F4208), there is a table with more detials.\n\n\n### Conventional Radar Datasets for Autonomous Driving\n| Dataset | Radar Type | Data Type| Annotation | Link |\n| ---- |----| ---- | ---- | ---- |\n| nuScenes | Continental ARS408 x5 | Sparse PC | 3D bbox, TrackID | [Website](https:\u002F\u002Fwww.nuscenes.org\u002F) |\n| DENSE| 77Ghz Long-Range Radar | Sparse PC | 3D bbox |[Website](https:\u002F\u002Fwww.uni-ulm.de\u002Fen\u002Fin\u002Fdriveu\u002Fprojects\u002Fdense-datasets) |\n| PixSet| TI AWR1843| Sparse PC | 3D bbox, TrackID|  [Website](https:\u002F\u002Fleddartech.com\u002Fsolutions\u002Fleddar-pixset-dataset\u002F)|\n| Radar Scenes | 77GHz Middle-Range Radar x4 | Dense PC |2D point-wise, TrackID| [Website](https:\u002F\u002Fradar-scenes.com\u002F)|\n| Pointillism | 2 TI AWR 1443 | PC | 3D bbox | [Github](https:\u002F\u002Fgithub.com\u002FKshitizbansal\u002Fpointillism-multi-radar-data) |\n| Zendar SAR | SAR | ADC, RD, PC| Pointwise Mask of Moving Vehicle |[Github](https:\u002F\u002Fgithub.com\u002FZendarInc\u002FZendarSDK) |\n| Cooperative Radars | 77GHz Radar x 3 | PC | Trajctory from GNSS-RTK | [Website](https:\u002F\u002Fieee-dataport.org\u002Fdocuments\u002Fradar-measurements-two-vehicles-three-cooperative-imaging-sensors) |\n| aiMotive| 77GHz LRR Radar x2(Front and Back) | PC | 3D bbox, TrackID | [Website](https:\u002F\u002Fgithub.com\u002Faimotive\u002Faimotive_dataset)|\n\n\u003Cbr>Comments: nuScenes, DENSE and Pixset are for sensor fusion, but not particularly address the role of radar. Radar scenes provides point-wise annotations for radar point cloud, but has no other modalities. Pointillism uses 2 radars with overlapped view. Zendar seems no longer available for downloading. AiMotive focuses on long-range 360 degree multi-sensor fusion.\n\n\n### Pre-CFAR Datasets for Detection\n| Dataset | Radar Type | Data Type| Annotation | Link |\n| ---- |----| ---- | ---- | ---- |\n| CRUW |  TI AWR1843 Ultra Short Range | RA | Pointlevel Object |[Website](https:\u002F\u002Fwww.cruwdataset.org\u002Fhome)|\n| CARRADA | TI AWR1843 Short Range | RA,RD,RAD | Pointwise, 2D bbox, Mask | [Website](https:\u002F\u002Farthurouaknine.github.io\u002Fcodeanddata\u002Fcarrada)|\n| RADDet | TI AWR1843 | RAD | 3D bbox for RAD tensor | [Github](https:\u002F\u002Fgithub.com\u002FZhangAoCanada\u002FRADDet) |\n| RaDICaL | TI IWR1443 | ADC | 2D bbox | [Website](https:\u002F\u002Fpublish.illinois.edu\u002Fradicaldata\u002F)|\n| GhentVRU | TI AWR1243 Short Range | RAD | Segmentation Mask for VRUs| [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9294399) |\n| RAMP-CNN | TI AWR 1843| ADC | 2D bbox | [Website](https:\u002F\u002Fgithub.com\u002FXiangyu-Gao\u002FRaw_ADC_radar_dataset_for_automotive_object_detection) |\n\n\u003Cbr>Comments: CARRADA is captured in clean scenarios, CRUW uses RA maps, RADDet provides annotations for RAD tensor, RADICaL provides raw ADC data and signal processing toolboxes, GhentVRU can be accssed by contacting with authors, ODA is for drones and provides event camera data.\n\n### 4D Radar Datasets\n| Dataset | Radar Type | Data Type| Annotation | Link |\n| ---- |----| ---- | ---- | ---- |\n| Astyx  Hires2019 | Astyx 6455 HiRes Middel Range| PC | 3D bbox|[Dateset](https:\u002F\u002Fgithub.com\u002Funder-the-radar\u002Fradar_dataset_astyx)|\n| View-of-Delft | ZF FRGen21 Short Range| PC | 3D bbox |[Website](https:\u002F\u002Fintelligent-vehicles.org\u002Fdatasets\u002Fview-of-delft\u002F)|\n| RADIal | Valeo Middel Range DDM | ADC,RAD,PC | Point-level Vehicle; Open Space Mask|[Github](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FRADIal)|\n| TJ4DRadSet | Oculii Eagle Long Range | PC |  3D bbox, TrackID| [Github](https:\u002F\u002Fgithub.com\u002FTJRadarLab\u002FTJ4DRadSet) |\n| K-Radar | Macnica RETINA | RAD |3D bbox, Track ID|[Github](https:\u002F\u002Fgithub.com\u002Fkaist-avelab\u002FK-Radar); [OpenReview](https:\u002F\u002Fopenreview.net\u002Fforum?id=W_bsDmzwaZ7) |\n| ZF 4DRadar Dataset |  ZF FRGen21 4D  | 3D | |  TBD [Github](https:\u002F\u002Fgithub.com\u002FZF4DRadSet\u002FZF-4DRadar-Dataset) |\n| ThermRad | Oculii Eagle | PC | 3D | TBD | \n| MSC-RAD4R | Oculii Eagle | PC | SLAM | [Website](https:\u002F\u002Fmscrad4r.github.io\u002Fhome\u002F) | \n\n\u003Cbr>Comments: Astyx is small, VoD focuses on VRU classification, RADIal's annotation is coarse but provides raw data, TJ4D features for its long range detection, K-Radar provides RAD tensor and 3D annotations.  ZF 4DRadar Dataset is not yet public available. \n\n### Specific Tasks\n| Dataset | Radar Type | Task | Link |\n| ---- |----| ---- | ---- |\n| HawkEye | SAR | Static vehicle classification | [Website](https:\u002F\u002Fjaydeng1019.github.io\u002FHawkEye\u002F)|\n| PREVENTION | Conti ARS308 + SRR208 x2 | Trajectory Prediction | [Website](https:\u002F\u002Fprevention-dataset.uah.es\u002F)|\n| SCORP | 76GHz | Open space segmentation | [Website](https:\u002F\u002Fsensorcortek.ai\u002Fpaper-and-datasets\u002F) |\n| Ghost | 77GHz long range *2  | Ghost object detection | [Github](https:\u002F\u002Fgithub.com\u002Fflkraus\u002Fghosts) |\n| Solinteraction Data | Soli | Tangible interactions| [Github](https:\u002F\u002Fgithub.com\u002Ftcboy88\u002Fsolinteractiondata) |\n| GROUNDED | Ground Penetrating Radar | Localization | [Website](https:\u002F\u002Flgprdata.com\u002F)|\n|FloW Dataset | TI AWR1843 | Floating waste detection | [Website](http:\u002F\u002Forca-tech.cn\u002Fdatasets\u002FFloW\u002FIntroduction) |\n| OLIMP | UWB + Continental ARS404|  Multi-sensor fusion for detection|[Website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fihsen-alouani\u002Fdatasets)|\n| DeepSense 6G | Radar+Lidar+Camera+GPS | Beam prediction | [Website](https:\u002F\u002Fdeepsense6g.net\u002F)|\n| CTA | Radar+Camera | Interference analysis | [Website](https:\u002F\u002Fedata.bham.ac.uk\u002F801\u002F) |\n| Radar^2 | TI AWR1843 | Spy radar detection | [Website](https:\u002F\u002Fieee-dataport.org\u002Fdocuments\u002Fradar2) |\n| Darting Pedestrians dataset | ZF FRGen21 Short Range | Darting pedestrians detection | [Website](https:\u002F\u002Fintelligent-vehicles.org\u002Fdatasets\u002Fdarting-pedestrians-dataset\u002F) |\n| ODA | 24GHz |  Obstacle detection and avoidance for drones | [Website](https:\u002F\u002Fgithub.com\u002Ftudelft\u002FODA_Dataset) |\n| Scattering Dataset | 77GHz | | [Website](https:\u002F\u002Fwww.fzd-datasets.de\u002Frcs\u002F)|\n| Radar Clutter Dataset | 77GHz | Clutter detections | [Website](https:\u002F\u002Fgithub.com\u002Fkopp-j\u002Fclutter-ds) |\n| Interference Dataset | 77GHz | Interference | [Website](https:\u002F\u002Fieee-dataport.org\u002Fdocuments\u002Fraw-adc-data-fmcw-radar-77-ghz-interference#files) |\n| OSDaR23 | Navtech Radar | Rail-specific object detection | [Website](https:\u002F\u002Fgithub.com\u002FDSD-DBS\u002Fraillabel) |\n\n### Odometry and Localization\n| Dataset | Radar Type | Task | Link |\n| ---- |----| ---- | ---- | \n| The Oxford Offroad Radar Dataset | Navtech Spinning Radar | Place Recognition |  [Website](oxford-robotics-institute.github.io\u002Foord-dataset) |\n| Oxford Radar Robocar | Navtech Spinning Radar |  Odometry, (Detection) | [Website](https:\u002F\u002Foxford-robotics-institute.github.io\u002Fradar-robotcar-dataset\u002F); [Detection Annotation](https:\u002F\u002Fgithub.com\u002Fqiank10\u002FMVDNet) |\n|RADIATE| Navtech Spinning  Radar | Odometry, Detection, Tracking | [Website](http:\u002F\u002Fpro.hw.ac.uk\u002Fradiate\u002Fdoc\u002Fdataset\u002F)|\n| MulRan | Navtech Spinning  Radar | Place Recognition |[Website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fmulran-pr\u002Fdataset)|\n| Boreas | Navtech Spinning  Radar|   Long-term Odometry, Localization, Detection | [Website](https:\u002F\u002Fwww.boreas.utias.utoronto.ca\u002F#\u002F)|\n| EU Long-term Dataset | Conti ARS 308 | Long-term SLAM | [Website](https:\u002F\u002Fepan-utbm.github.io\u002Futbm_robocar_dataset\u002F)|\n| ColoRadar | TI AWR2243 Cascade + AWR1843 |Odometry |  [Website](https:\u002F\u002Farpg.github.io\u002Fcoloradar\u002F) |\n| USVInland | TI AWR1843 | SLAM in inland waterways, Water segmentation| [Website](http:\u002F\u002Forca-tech.cn\u002Fdatasets\u002FUSVInland\u002FIntroduction) |\n| Endeavour Radar Dataset | Conti ARS 430 x5 | Odometry | [Website](https:\u002F\u002Fgloryhry.github.io\u002F2021\u002F06\u002F25\u002FEndeavour_Radar_Dataset.html)|\n| OdomBeyondVision |  TI AWR1843 | Odometry | [Website](https:\u002F\u002Fgithub.com\u002FMAPS-Lab\u002FOdomBeyondVision) |\n\n\n### Gesture\n| Dataset | Radar Type | Data Type | Task | Link |\n| ---- |----| ---- | ---- | ---- | \n| DopNet | 24GHz | Spectrogram | Gesture | [Website](http:\u002F\u002Fdop-net.com\u002F)|\n| MCD-Gesture | 77GHz | RAD tensor |Gesture | [Website](https:\u002F\u002Fgithub.com\u002FDI-HGR\u002Fcross_domain_gesture_dataset)|\n| DeepSoli | 60GHz | RD map | Gesture | [Website](https:\u002F\u002Fgithub.com\u002Fsimonwsw\u002Fdeep-soli) | \n| Pantomime | TI IWR1443 | PC | Gesture  | [Dataset](https:\u002F\u002Fzenodo.org\u002Frecord\u002F4459969) |\n| MIMOGR | | RADT | Gesutre | [Website](https:\u002F\u002Fgithub.com\u002FTkwer\u002FGesture-Recognition-Based-on-mmwave-MIMO-Radar) |\n\n### Human Activity and Reconstruction\n| Dataset | Radar Type | Data Type | Task | Link |\n| ---- |----| ---- | ---- | ---- | \n| Radar signatures of human activities  | 5.8 GHz | ADC | Human activities | [Dataset](http:\u002F\u002Fresearchdata.gla.ac.uk\u002F848\u002F) |\n| Ci4R human activity dataset | 77GHz & 24GHz & 10GHz |Spectrogram | Human activities | [Website](https:\u002F\u002Fgithub.com\u002Fci4r\u002FCI4R-Activity-Recognition-datasets\u002F) |\n| RadHAR | 77GHz | Point Cloud | Human activities  | [Website](https:\u002F\u002Fgithub.com\u002Fnesl\u002FRadHAR) |\n| mRI | 77GHz| PC, RGBD camera, IMU | Human pose estimation  | [Website](https:\u002F\u002Fsizhean.github.io\u002Fmri)|\n| mmBody  | Arbe Phoenix 4D Radar | PC, RGBD  | 3D body reconstruction | [Website](https:\u002F\u002Fchen3110.github.io\u002Fmmbody\u002Findex.html) ||\n| HuPR | 2 TI 1843 | RAD | Pose | [Github](https:\u002F\u002Fgithub.com\u002Frobert80203\u002FHuPR-A-Benchmark-for-Human-Pose-Estimation-Using-Millimeter-Wave-Radar) |\n\n\n\n### Vital Sign\n| Dataset | Radar Type | Data Type | Task | Link |\n| ---- |----| ---- | ---- | ---- | \n| Child Vital Sign | 60GHz| ADC  | heart beat, respiration | [Dataset](https:\u002F\u002Ffigshare.com\u002Fs\u002F936cf9f0dd25296495d3) |\n| GUARDIAN  Vital Sign | 24GHz | IQ | heart beat, respiration | [Dataset 1](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FA_dataset_of_clinically_recorded_radar_vital_signs_with_synchronised_reference_sensor_signals\u002F12186516?file=22515785) [Dataset 2](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FA_dataset_of_radar-recorded_heart_sounds_and_vital_signs_including_synchronised_reference_sensor_signals\u002F9691544?backTo=\u002Fcollections\u002FGUARDIAN_Vital_Sign_Data\u002F4633958)|\n| Multi-Person Localization and Vital Sign Estimation Radar Dataset |  | |  | [Dataset](https:\u002F\u002Fieee-dataport.org\u002Fopen-access\u002Fmulti-person-localization-and-vital-sign-estimation-radar-dataset) |\n\n---\n\n## Radar Toolbox\n### Simulation\nRadarSimPy: [Code](https:\u002F\u002Fgithub.com\u002Frookiepeng\u002Fradarsimpy);\u003Cbr>\nVirtual Radar: [Code](https:\u002F\u002Fgithub.com\u002Fchstetco\u002Fvirtualradar);\u003Cbr>\nMaxRay: [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.01751);\u003Cbr>\nRadaRays: [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10845807), [Code](https:\u002F\u002Fgithub.com\u002Fuos\u002Fradarays), [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fLH8JPYk67o)\n\n### TI Signal Processing SDK:\nRaDICaL's Toolbox: [SDK](https:\u002F\u002Fgithub.com\u002Fmoodoki\u002Fradical_sdk); \u003Cbr>PyRapid: [SDK](http:\u002F\u002Fradar.alizadeh.ca);\u003Cbr>OpenRadar : [SDK](https:\u002F\u002Fgithub.com\u002Fpresenseradar\u002Fopenradar);\u003Cbr>Pymmw: [SDK](https:\u002F\u002Fgithub.com\u002Fm6c7l\u002Fpymmw);\u003Cbr>Open radar initiative: [SDK](https:\u002F\u002Fgithub.com\u002Fopenradarinitiative);\u003Cbr>RADIal's Emptyband-DDM Script: [Code](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FRADIal\u002Ftree\u002Fmain\u002FSignalProcessing) \n\n### Official SDK:\nNXP Premium Radar SDK: [Link](https:\u002F\u002Fwww.nxp.com\u002Fdesign\u002Fautomotive-software-and-tools\u002Fpremium-radar-sdk-advanced-radar-processing:PREMIUM-RADAR-SDK);\u003Cbr>TI mmWAVE Studio: [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FMMWAVE-STUDIO);\u003Cbr>TI Toolbox: [Link](https:\u002F\u002Fdev.ti.com\u002Ftirex\u002Fexplore\u002Fnode?node=AHJY4qNCowO17wH-P2ICKQ);\u003Cbr>Matlab Radar Toolbox: [Link](https:\u002F\u002Fuk.mathworks.com\u002Fproducts\u002Fradar.html)\n\n### Data Capturing:\nTI Radar and Camera in Python:[Code](https:\u002F\u002Fgithub.com\u002Fyizhou-wang\u002Fcr-data-collector);\u003Cbr>\nAinstein Radar ROS Node: [ROS Node](https:\u002F\u002Fgithub.com\u002FAinsteinAI\u002Fainstein_radar);\u003Cbr>Continental ARS 408 ROS Node: [ROS Node](https:\u002F\u002Fgitlab.com\u002FApexAI\u002Fautowareclass2020\u002F-\u002Ftree\u002Fmaster\u002Fcode\u002Fsrc\u002F09_Perception_Radar\u002FRadar-Hands-On-WS);\u003Cbr>TI mmWave ROS Driver: [Guide](https:\u002F\u002Fdev.ti.com\u002Ftirex\u002Fexplore\u002Fnode?node=ADINBw2NDaxb6JeW7V-lMQ__VLyFKFf__LATEST&search=ROS);\u003Cbr>RaDICaL's TI ROS Node: [ROS Node](https:\u002F\u002Fgithub.com\u002Fmoodoki\u002Fiwr_raw_rosnode);\u003Cbr>UoA's TI ROS Package: [ROS Node](https:\u002F\u002Fgithub.com\u002Fradar-lab\u002Fti_mmwave_rospkg)\n\n---\n\n## Seminars and Workshops\n\n* 2021 ICRA Radar Perception for All-Weather Autonomy [[Website]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fradar-robotics\u002Fhome)\n* 2021 ICASSP Recent Advances in mmWave Radar Sensing for Autonomous Vehicles [[Website]](https:\u002F\u002Fwww.2021.ieeeicassp.org\u002FPapers\u002FViewSession_MS.asp?Sessionid=1280)\n* Radar in Action Series by Fraunhofer FHR [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fhashtag\u002Fradarinaction)\n* IEEE AESS Virtual Distinguished Lecturer Webinar Series [[Website]](https:\u002F\u002Fieee-aess.org\u002Factivities\u002Feducational-activities\u002Fdistinguished-lecturers)\n* Journal of Radar Webinar Series (in Chinese) [[Video]](https:\u002F\u002Fspace.bilibili.com\u002F1288394672)\n\n* Markus Gardill: Automotive Radar – An Overview on State-of-the-Art Technology [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=P-C6_4ceY64&t=2416s)\n* Markus Gardill: Automotive Radar – A Signal Processing Perspective on Current Technology and Future Systems [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IxoPYhXY30k&t=11s)[[Slides]](https:\u002F\u002Fcloud.gardill.net\u002Fs\u002FtjoSLSB7fXWTEBb)\n* Francesco Fioranelli: Radar Old but Gold- current research challenges and activities in radar micro-Doppler signatures [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ysL6rk-4L9o&list=PLa5-fgjZm9MtJBtb6M3YplIDSdgr1m94n&index=3)\n* Andrej Karpathy from Tesla:  [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=g6bOwQdCJrc)\n* Ole Schumann: Radar Perception for Automated Driving – Data and Methods [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UQL7_Zy2Kjg&t=73s)\n* Sani Ronen from Arbe: Using AI layer to transform HR radar into insights for Autonomous Driving applications [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=TtJW-c02YH8)\n* Stefan Haag: Co-Development of Automatic Annotation for ML and Sensor Fusion Improvement System  [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ANbmXg2TxlE)\n* Arthur Ouaknine: Deep Learning & Scene Understanding for autonomous vehicle [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sOOvnTCnhPg)\n* Paul Newman: The Road to Anywhere-Autonomy [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MzYQMRG9HW0)\n* Jaime Lien: Soli: Millimeter-wave radar for touchless interaction [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JFr8Whnx630)\n* Accelerating end-to-end Development of Software-Defined 4D Imaging Radar [[Videp]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QPoj1zM2vCs)\n* Radar-Imaging - An Introduction to the Theory Behind [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ej2smyTZLHE)\n* NXP - Radar Experts Discuss the Evolution of Automotive Radar [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RfJiiSlesyE&t=347s)\n* Need to Successfully Design a Milimeter-Wave Automotive Radar Antenna? [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0lK8qJSWY_c)\n* Webinar SAR Imaging using Ancortek’s Software Defined Radars [[Video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BMSVvQJYCIs)\n* TI: Managing interference in FMCW radar systems [[Video]](https:\u002F\u002Ftraining.ti.com\u002Fmanaging-interference-fmcw-radar-systems)\n\n\n---\n\n## Review Papers\n\nRadar Hardware:\n* [Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6127923)\n* [Radar-on-Chip\u002Fin-Package in Autonomous Driving Vehicles and Intelligent Transport Systems](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8830483)\n* [Antenna Concepts for Millimeter-Wave Automotive Radar Sensors](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6165323)\n* [System Performance of a 79 GHz High-Resolution 4D Imaging MIMO Radar With 1728 Virtual Channels](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9866614)\n\n\nRadar Signal Processing:\n* General: [The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828025\u002F)\n* Signal Processing: [Automotive Radar Signal Processing: Research Directions and Practical Challenges](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9369027)\n* Signal Processing: [Automotive Radars A review of signal processing techniques](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7870764)\n* Signal Processing: [Advances in Automotive Radar\nA framework on computationally efficient high-resolution frequency estimation](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7870737)\n* MIMO: [MIMO Radar for Advanced Driver-Assistance Systems and Autonomous Driving: Advantages and Challenges](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9127853)\n* DOA: [Calibration and Direction-of-Arrival Estimation of mm-Wave Radars: A Practical Introduction](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9099537)\n* Digital Radar: [High-Performance Automotive Radar: A Review of Signal Processing Algorithms and Modulation Schemes](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828004)\n* Micro-Doppler: [Micro-Doppler Effect in Radar: Phenomenon, Model, and Simulation Study](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1603402)\n* Dynamic Range: [Dynamic Range Considerations for Modern Digital\nArray Radars](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9114607)\n* Phase Noise: [On the Safe Road Toward Autonomous Driving: Phase Noise Monitoring in Radar Sensors for Functional Safety Compliance](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8827996)\n* Phase Noise: [Detailed Analysis and Modeling of Phase Noise and Systematic Phase Distortions in FMCW Radar Systems](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9875949)\n* Interference: [Radar Interference Mitigation for Automated Driving: Exploring Proactive Strategies](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9127843)\n* Interference: [Interference in Automotive Radar Systems: Characteristics, Mitigation Techniques, and Current and Future Research](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828037)\n\n\n\nAutomotive Radar Applications:\n* Detection,Fusion for Autonomous Driving: [Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F22\u002F11\u002F4208)\n* Signal Processing: [Automotive Radar Signal Processing: Research Directions and Practical Challenges](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9369027)\n* Interference: [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9802083)\n* Semantic Understanding: [Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-658-23751-6_1)\n* Radar vs Lidar: [Comparative Analysis of Radar and Lidar Technologies for Automotive Applications](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9760734\u002F)\n\nOther Radar Applications:\n* Human Activity Recognition: [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3447744)\n* Gesture: [Motion Sensing Using Radar: Gesture Interaction and Beyond](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8755821)\n* Vital Sign: [Contactless Radar-Based Sensors: Recent Advances in Vital-Signs Monitoring of Multiple Subjects](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9785580)\n* UAV: [Radar Perception for Autonomous Unmanned Aerial Vehicles: a Survey](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3522784.3522787)\n\nGeneral Object Detection:\n* [3D Object Detection from Images for Autonomous Driving: A Survey](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.02980)\n* [Deep Learning for 3D Point Clouds: A Survey](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.12033)\n* [Attention Mechanisms in Computer Vision: A Survey](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.07624)\n* [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.10671)\n\n\n\nSensor Fusion:\n\n* Learning: [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12735)\n* Learning: [Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9000872)\n* Traditional: [Multisensor data fusion: A review of the state-of-the-art](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1566253511000558)\n* Information: [Information Decomposition of Target Effects from Multi-Source Interactions: Perspectives on Previous, Current and FutureWork](https:\u002F\u002Fwww.mdpi.com\u002F1099-4300\u002F20\u002F4\u002F307)\n* Uncertainty: [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-021-05946-3)\n* Conformal Prediction: [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.10671)\n\n\n\n\n---\n\n## Recommended Books and Tutorials\n### Radar Textbook\n* Fundamentals of Radar Signal Processing by Mark A. Richard\n* Radar Systems Analysis and Design using Matlab by Bassem R. Mahafza \n### Online Course\n* [Radar: Introduction to Radar Systems](https:\u002F\u002Fwww.ll.mit.edu\u002Foutreach\u002Fradar-introduction-radar-systems-online-course)\n* [Build a Radar](https:\u002F\u002Fllx.mit.edu\u002Fcourses\u002Fcourse-v1:MITLL+MITLLx01+Q2_2019\u002Fabout)\n* [Radar Systems Engineering](http:\u002F\u002Fradar-course.org\u002F)\n* [Adaptive Antennas and Phased Arrays](https:\u002F\u002Fwww.ll.mit.edu\u002Foutreach\u002Fadaptive-antennas-and-phased-arrays-online-course)\n\n### Signal Processing\n* [Introduction to mmwaveSensing: FMCW Radars](https:\u002F\u002Ftraining.ti.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fdocs\u002FmmwaveSensing-FMCW-offlineviewing_0.pdf)\n* [The fundamentals of millimeter wave sensors](https:\u002F\u002Fwww.ti.com\u002Flit\u002Fwp\u002Fspyy005a\u002Fspyy005a.pdf?ts=1619205965675)\n* [Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9658500)\n* [Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9908570)\n### Waveform Comparison\n* [Analysis and Comparison of MIMO Radar Waveforms](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7060251)\n### Quadrature Signal\n* [Quadrature Signals: Complex, But Not Complicated](https:\u002F\u002Fdspguru.com\u002Fdsp\u002Ftutorials\u002Fquadrature-signals\u002F) \n* [Using a complex-baseband architecture in FMCW radar systems](https:\u002F\u002Fwww.ti.com\u002Flit\u002Fwp\u002Fspyy007\u002Fspyy007.pdf)\n### MIMO\n* [TI MIMO radar](https:\u002F\u002Fwww.ti.com\u002Flit\u002Fan\u002Fswra554a\u002Fswra554a.pdf)\n* [TI EmptyBand DDM (Chinese)]() [(English)]()\n\n---\n\n## Radar Signature\n### PointCloud\n* 2022-A Data-driven Approach for Stochastic Modeling of Automotive Radar Detections for Extended Objects __`GeMic`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9783497)\n* 2020-Performance Evaluation Of Wide Aperture Radar For Automotive Applications __`RadarConf`__; __`0.1Deg`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9266609)\n* 2018-Radar and Lidar Target Signatures of Various Object Types and Evaluation of Extended Object Tracking Methods for Autonomous Driving Applications [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8455395)\n* 2017-Radar Reflection Characteristics of Vehicles for Contour and Feature Estimation __`FUSION`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8126352)\n\n### RCS\n* 2021-Machine Learning-Based Target Classification for MMW Radar in Autonomous Driving [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9319548)\n* 2021-Performance evaluation of a state-of-the-art automotive radar and corresponding modeling approaches based on a large labeled dataset [Paper](https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Ffull\u002F10.1080\u002F15472450.2021.1959328)\n* 2021-Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms __`RadarConf`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455239)\n* 2018-Review of Radar Classification & RCS Characterisation Techniques for Small UAVs or Drones [Paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1049\u002Fiet-rsn.2018.0020)\n\n### Phase\n* 2019-Phase-Based Target Classification Using Neural Network in Automotive Radar Systems __`RadarConf`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8835725)\n\n### Motion\n* 2021-Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms __`RadarConf`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455239)\n* 2020-New Radar Micro-Doppler Tag for Road Safety Based on the Signature of Rotating Backscatters [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9311635)\n* 2019-Motion sensing using radar: Gesture interaction and beyond [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8755821)\n\n### Polarimetric\n* 2019-Polarimetric Signatures of a Passenger Car [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8890117)\n* 2018-Performance Analysis of 79 GHz Polarimetric Radar Sensors for Autonomous Driving [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8249142)\n* 2018-Autonomous Driving Features based on 79 GHz Polarimetric Radar Data [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8546632)\n\n---\n\n## Calibration\n### Radar\n* 2022-Radar Calibration by Corner Reflectors with Mass-production Errors [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784534)\n* 2022-A novel method for calibration and verification of road side millimetre-wave radar [Paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1049\u002Fitr2.12151)\n* 2021-Auto-Calibration of Automotive Radars in Operational Mode Using Simultaneous Localisation and Mapping __`TVT`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9353252)\n* 2020-Motion Based Online Calibration for 4D Imaging Radar in Autonomous Driving Applications __`GeMic`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9080233)\n* 2018-Multi-Radar Self-Calibration Method using High-Definition Digital Maps for Autonomous Driving __`ITSC`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8569272\u002F)\n\n### Radar-Camera\n* 2021-A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration __`ICRA`__; __`Motion`__ ; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9561938)\n* 2021-Spatio-Temporal Multisensor Calibration Based on Gaussian Processes Moving Object Tracking __`ToR`__; __`Trajectory`__ ; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9387269)\n* 2019-Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems __`ITSC`__; __`NN`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8917135)\n* 2015-Radar and vision sensors calibration for outdoor 3D reconstruction __`ICRA`__;  [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7139473)\n* 2004-Obstacle Detection Using Millimeter-wave Radar and Its Visualization on Image Sequence __`ICPR`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1334537\u002F)\n\n### Radar-Lidar\n* 2020-Extrinsic and Temporal Calibration of Automotive Radar and 3D LiDAR __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9341715)\n* 2020-Automatic Targetless Extrinsic Calibration of Multiple 3D LiDARs and Radars __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9340866\u002F)\n* 2017-Extrinsic 6DoF calibration of 3D LiDAR and radar __`RCS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8098688)\n\n### Radar-Lidar-Camera\n* Continuous Target-free Extrinsic Calibration of a Multi-Sensor System from a Sequence of Static Viewpoints [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03785)\n* 2022-OpenCalib: A multi-sensor calibration toolbox for autonomous driving [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14087); [Code](https:\u002F\u002Fgithub.com\u002FPJLab-ADG\u002FSensorsCalibration)\n* 2021-An Joint Extrinsic Calibration Tool for Radar, Camera and Lidar __`TIV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9380784); [Code](https:\u002F\u002Fgithub.com\u002Ftudelft-iv\u002Fmulti_sensor_calibration)\n* 2021-Online multi-sensor calibration based on moving object tracking [Paper](https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Ffull\u002F10.1080\u002F01691864.2020.1819874)\n* 2019-Extrinsic 6DoF Calibration of a Radar – LiDAR– Camera System Enhanced by Radar Cross Section Estimates Evaluation [Paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0921889018301994)\n\n---\n\n## Labelling\n* 2021-Spatio-Temporal Consistency for Semi-supervised Learning Using 3D Radar Cubes __`IV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9575247)\n* 2021-Automatic labeling of vulnerable road users in multi-sensor data __`ITSC`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564692)\n* 2021-Rethinking of Radar’s Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment __`CVPRW`__; __`CRUW`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021W\u002FWAD\u002Fhtml\u002FWang_Rethinking_of_Radars_Role_A_Camera-Radar_Dataset_and_Systematic_Annotator_CVPRW_2021_paper.html)\n* 2021-RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users __`CRV`__; __`RADDet`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9469418)\n* 2020-CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations __`ICPR`__; __`CARRADA`__;  [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9413181)\n* 2020-Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.01993)\n* 2020-Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF) [Paper](https:\u002F\u002Fml4ad.github.io\u002Ffiles\u002Fpapers2020\u002FAnnotating%20Automotive%20Radar%20efficiently:%20Semantic%20Radar%20Labeling%20Framework%20(SeRaLF).pdf)\n* 2020- RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar __`IV`__; __`Oxford`__; __`PoseChain`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9304674)\n* 2019-Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS __`ICMIM`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8726801)\n\n---\n\n\n## Data Augmentation\n* 2022-Multi-class Road User Detection with 3+1D Radar in the View-of-Delft Dataset  __`RAL`__; __`PC`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9699098)\n* 2021-Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra __`RadarConf`__; __`RA`__; __`Corruption`__ ; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9455269)\n* 2021-Data Augmentation in Time and Doppler Frequency Domain for Radar-based Gesture Recognition __`EuRad`__; __`Spectrogram`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784553)\n* 2020-RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition __`RA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9249018)\n* 2020-RADIO Parameterized Generative Radar Data Augmentation for Small Datasets __`RA`__; [Paper](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F10\u002F11\u002F3861)\n* 2016-Convolutional Neural Network With Data Augmentation for SAR Target Recognition __`GRSL`__; __`SAR`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7393462)\n\n\n---\n\n## Simulator\n* 2024-RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation  __`CVPR`__;\n* 2021-MaxRay: A Raytracing-based Integrated Sensing and Communication Framework __`OpenSoucre`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9743510)\n* 2021-Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics __`RAL`__; __`OpenSoucre`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9387149); [Code](https:\u002F\u002Fgithub.com\u002Fchstetco\u002Fvirtualradar)\n* 2020-Scalable and Physical Radar Sensor Simulation for Interacting Digital Twins[Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9205224)\n\n### Artifacts\n* 2020-Simulator Design for Interference Analysis in Complex Automotive Multi-User Traffic Scenarios [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9266318)\n* 2019-Modeling and Simulation of Radar Sensor Artifacts for Virtual Testing of Autonomous Driving [Paper](https:\u002F\u002Fmediatum.ub.tum.de\u002F1535151)\n\n### Evaluation\n* 2021-Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving __`RadarConf`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455235)\n* 2021-A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving __`IV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564521)\n* 2018-Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving __`ITSC`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8569423)\n\n\n---\n\n## Generative Model\n* 2021-There and Back Again: Learning to Simulate Radar Data for Real-World Applications __`ICRA`__; __`Simulation_to_RA`__; __`Categorical_VAE`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.14389)\n* 2020-L2R GAN: LiDAR-to-Radar Translation __`ACCV`__; __`Lidar_OGM_to_RD`__; __`Oxford`__ ;__`cGAN`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2020\u002Fpapers\u002FWang_L2R_GAN_LiDAR-to-Radar_Translation_ACCV_2020_paper.pdf)\n* 2020-GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies __`Journal`__; __`RD_to_Image`__; __`Categorical_VAE`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.08948)\n* 2019-Automotive radar and camera fusion using Generative Adversarial Networks __`Journal`__; __`Radar_OGM_to_Image`__; __`cGAN`__; [Paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1077314219300530)\n* 2017-Deep Stochastic Radar Models __`IV`__; __`Scene_to_RA`__; __`VAE`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7995697)\n\n### Doppler\n* 2021-IMU2Doppler: Cross-Modal Domain Adaptation for Doppler-based Activity Recognition Using IMU Data __`IMWUT`__; __`IMU`__; [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3494994)\n* 2021-Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition __`CHI`__; __`Video`__; [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3411764.3445138)\n\n---\n\n## Testing\n* 2021-Millimeter-Wave Radar in-the-Loop Testing for Intelligent Vehicles __`TITS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9507048)\n* 2020-On the Testing of Advanced Automotive Radar Sensors by Means of Target Simulators [Paper](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F9\u002F2714)\n* 2017-Surrogate Bicycle Design for Millimeter-Wave Automotive Radar Pre-Collision Testing __`TITS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7829378)\n\n\n---\n\n## MIMO Calibration\n* 2021-Auto-Calibration of Automotive Radars in Operational Mode Using Simultaneous Localisation and Mapping __`TVT`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9353252)\n* 2021-A Practical Concept for Precise Calibration of MIMO Radar Systems __`EuRAD`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784495)\n\n---\n\n## Detector\n* 2022-A Novel Radar Point Cloud Generation Method for Robot Environment Perception [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9823311)\n* 2021-DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9547416)\n* 2020-Object surface estimation from radar images [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9054622)\n* 2020-Deep temporal detection - A machine learning approach to multiple-dwell target detection [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9114828)\n* 2019-DL-CFAR: a Novel CFAR Target Detection Method Based on Deep Learning [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8891420)\n* 2019-Feature Detection With a Constant FAR in Sparse 3-D Point Cloud Data [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8924890)\n\n## Super Resolution\n* 2024-DART: Implicit Doppler Tomography for Radar Novel View Synthesis __`CVPR`__;[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.03896); [Codes](https:\u002F\u002Fgithub.com\u002FWiseLabCMU\u002Fdart)\n* 2023-Data-driven Spatial Super-Resolution for FMCW mmWave Sensing Systems [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10287476)\n* 2023-Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.09839)\n* 2023-Azimuth Super-Resolution for FMCW Radar in Autonomous Driving __`CVPR`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLi_Azimuth_Super-Resolution_for_FMCW_Radar_in_Autonomous_Driving_CVPR_2023_paper.html)\n* 2023-Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10106481)\n* 2022-A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9674901)\n* 2020-Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9412858)\n\n## Clustering\n\n* 2021-Supervised Noise Reduction for Clustering on Automotive 4D Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9659953)\n* 2019-A Multi-Stage Clustering Framework for Automotive Radar Data [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8916873)\n* 2019-Robust and Adaptive Radar Elliptical Density-Based Spatial Clustering and labeling for mmWave Radar Point Cloud Data [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9048869)\n* 2018-Supervised Clustering for Radar Applications On the Way to Radar Instance Segmentation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8443534)\n* 2016-Adaptive Clustering for Contour Estimation of Vehicles for High-Resolution Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7533930)\n* 2012- Grid-Based DBSCAN for Clustering Extended Objects in Radar Data [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6232167)\n\n\n## Denoising\n* Deep Convolutional Autoencoder Applied for Noise Reduction in Range-Doppler Maps of FMCW Radars\n* Learning from Natural Noise to Denoise Micro-Doppler Spectrogram\n\n\n---\n\n## TI Reference Designs\n* TIDEP-01027 High-end corner radar reference design __`AWR2944`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01027)\n* TIDEP-01025 mmWave diagnostic and monitoring reference design [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01025)\n* TIDEP-01024 Obstacle detection reference design using 76-Ghz to 81-GHz antenna-on-package (AoP) mmWave sensor __`AWR1843AOP`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01024)\n* TIDEP-01023 Child-presence and occupant-detection reference design using 60-GHz antenna-on-package mmWave sensor __`AWR6843AOP`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01023)\n* TIDEP-01021 Beamsteering for corner radar reference design __`AWR1843BOOST`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01021)\n* TIDEP-01018 Automated doors reference design using TI mmWave sensors __`IWR6843ISK`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01018)\n* TIDEP-01013 Gesture controlled HMI with mmWave sensors and Sitara™ processors reference design __` IWR6843ISK`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01013)\n* TIDEP-01012 Imaging radar using cascaded mmWave sensor reference design __`AWR2243`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01012)\n* TIDEP-01011 Automated parking system reference design using 77-GHz mmWave sensor __`AWR1843`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01011)\n* TIDEP-01010 Area scanner using mmWave Sensor with integrated antenna-on-package reference design __`IWR6843`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01010)\n* TIDEP-0104 Obstacle detection reference design using 77-GHz mmWave sensor __`AWR1642`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0104)\n* TIDEP-01006 Autonomous robot reference design using ROS on Sitara™ MPU & antenna-on-package mmWave sensors __`IWR6843`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01006)\n* TIDEP-01003 Zone occupancy detection using mmWave sensor reference design __` IWR1443BOOST`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01003) \n* TIDEP-01001 Vehicle occupant detection reference design __`AWR6843`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01001)\n* TIDEP-01000 People Counting and Tracking Reference Design Using mmWave Radar Sensor __`IWR6843`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01000)\n* TIDEP-0094 80m-Range Object Detection Reference Design With Integrated Single-Chip mmWave Sensor  __`IWR1642`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0094)\n* TIDEP-0092 Short-range radar (SRR) reference design __`IWR1642`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0092)\n* TIDEP-0091 Power optimization for 77GHz-level transmitter reference design __`IWR1443`__; [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0091)\n* TIDEP-0090 Traffic Monitoring Object Detection and Tracking Reference Design Using mmWave Radar Sensor [Link](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0090)\n\n\n---\n\n\n\n### Classification of Clusters\n* 2015-[Making Bertha See Even More](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7161279)\n* 2017-[Comparison of Random Forest and Long Short-Term Memory Network Performances in Classification Tasks Using Radar](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8126350)\n* 2018-[Radar-based Feature Design and Multiclass Classification for Road User Recognition](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8500607)\n* 2019-[Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8813773)\n* 2020-[Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9190338)\n* 2021-[Deep Learning for Automotive Object Classification with Radar Reflections](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455334) \n\n\n\n---\n\n## Object Detection\n* 2024-Bootstrapping Autonomous Radars with Self-Supervised Learning __`CVPR`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.04519)\n* 2024-RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features __`CVPR`__;\n* 2024-SIRA: Scalable Inter-frame Relation and Association for Radar Perception __`CVPR`__;\n* 2023-SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar __`VOD`__; __`TJ4D`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.10784)\n* 2023-PeakConv: Learning Peak Receptive Field for Radar Semantic Segmentation __`CVPR`__;  [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.html)\n* 2023-Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection  __`KRadar`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06342)\n* Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information __`Oxford`__; __`RADIATE`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10450)\n* 2022-A recurrent CNN for online object detection on raw radar frames__`CARRADA`__; __`ROD`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.11172)\n* 2022-Radatron: Accurate Detection Using Multi-Resolution Cascaded MIMO Radar __`ECCV`__; [Paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-19842-7_10)\n* 2022-Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data  __`Segmentation`__; __`RadarScenes`__; __`RAL`__; [Paper](https:\u002F\u002Fwww.ipb.uni-bonn.de\u002Fwp-content\u002Fpapercite-data\u002Fpdf\u002Fzeller2022ral.pdf)\n* 2022-3D Object Detection for Multi-frame 4D Automotive Millimeter-wave Radar Point Cloud  __`3DDetection`__;  __`TJ4D`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9944629)\n* 2022-mmWave-YOLO: A mmWave Imaging Radar-Based Real-Time Multiclass Object Recognition System for ADAS Applications [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9777730)\n* 2022-NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.14499)\n* 2022-ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals __`Segmentation`__;__`CARRADA`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.12940)\n* 2022-Raw High-Definition Radar for Multi-Task Learning __`CVPR`__; __`RADIAL`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FRebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html)\n* 2022-Exploiting Temporal Relations on Radar Perception for Autonomous Driving __`CVPR`__; __`Oxford`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html)\n* 2022-Deep Instance Segmentation with Automotive Radar Detection Points __`TIV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9762032)\n* 2022-Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar\n* 2022-HARadNet: Anchor-free target detection for radar point clouds using hierarchical attention and multi-task learning\n* Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-cloud Data\n* 2021-Radar Voxel Fusion for 3D Object Detection [Paper](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F12\u002F5598); [Code](https:\u002F\u002Fgithub.com\u002FTUMFTM\u002FRadarVoxelFusionNet)\n* 2021-Quantification of Uncertainties in Deep Learning - based Environment Perception [Paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.03018.pdf)\n* 2021-Graph Convolutional Networks for 3D Object Detection on Radar Data __`ICCVW`__; __`3DDetection`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021W\u002FAVVision\u002Fhtml\u002FMeyer_Graph_Convolutional_Networks_for_3D_Object_Detection_on_Radar_Data_ICCVW_2021_paper.html)\n* 2021-High-resolution radar road segmentation using weakly supervised learning __`Segmentation`__; [Paper](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-020-00288-6)[Code](https:\u002F\u002Fgithub.com\u002Fitaiorr\u002Fradar_road_seg)\n* 2021-Multi-View Radar Semantic Segmentation __`ICCVW`__; __`CARRADA`__;  __`Segmentation`__;  [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fhtml\u002FOuaknine_Multi-View_Radar_Semantic_Segmentation_ICCV_2021_paper.html); [Code](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FMVRSS)\n* 2021-RPFA-Net: a 4D RaDAR Pillar Feature Attention Network for 3D Object Detection [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564754\u002F)\n* 2021-Towards Pedestrian Detection in Radar Point Clouds with Pointnets [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459066.3459067)\n* 2021-Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [Paper](https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F1742-6596\u002F1924\u002F1\u002F012003\u002Fpdf)\n* 2021-A Neural Network Based System for Efficient Semantic Segmentation of Radar Point Clouds [Paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11063-021-10544-4)\n* 2020-Leveraging radar features to improve point clouds segmentation with neural networks [Paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-48791-1_8)\n* 2019-[Experiments with mmWave Automotive Radar Test-bed](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9048939)\n* 2019-[Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCVW_2019\u002Fhtml\u002FCVRSUAD\u002FMajor_Vehicle_Detection_With_Automotive_Radar_Using_Deep_Learning_on_Range-Azimuth-Doppler_ICCVW_2019_paper.html)\n* 2020-[CNN Based Road User Detection Using the 3D Radar Cube](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8962258)\n* 2020-[RAMP-CNN A Novel Neural Network for Enhanced Automotive Radar Object Recognition](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9249018)\n* 2019-[2D Car Detection in Radar Data with PointNets](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917000)\n* 2020-[Detection and Tracking on Automotive Radar Data with Deep Learning](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9190261)\n* 2020-[Pointillism Accurate 3D Bounding Box Estimation with Multi-Radars](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3384419.3430783)\n* 2020-[Radar-based 2D Car Detection Using Deep Neural Networks](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9294546)\n* 2021-[Radar Voxel Fusion for 3D Object Detection](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F12\u002F5598)\n* 2021-[Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F6\u002F2599)\n* 2019-[Deep Radar Detector](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8835792)\n* 2019-[mID Tracking and Identifying People with Millimeter Wave Radar](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8804831)\n* 2020-[Improved and Optimal DBSCAN for Embedded Applications Using High-Resolution Automotive Radar](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9253774)\n* 2020-[MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9114662)\n* 2020-[Through Fog High Resolution Imaging Using Millimeter Wave Radar](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FGuan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html)\n* 2020-[Deep Learning on Radar Centric 3D Object Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.00851)\n* 2021-[Radar Transformer An Object Classification Network Based on 4D MMW Imaging Radar](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F21\u002F11\u002F3854)\n* 2021-[mmPose-NLP A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.10327)\n\n\n### Pre-CFAR Data\n#### Range Azimuth Map\n* 2019-[Deep Learning-based Object Classification on Automotive Radar Spectra](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8835775)\n* 2020-[Image Segmentation and Region Classification in Automotive High-Resolution Radar Imagery](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9288850)\n* 2020-[YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F10\u002F2897)\n* 2020-[300 GHz radar object recognition based on deep neural networks and transfer learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.03157)\n* 2020-[2020-Probabilistic Oriented Object Detection in Automotive Radar](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2020\u002Fhtml\u002Fw6\u002FDong_Probabilistic_Oriented_Object_Detection_in_Automotive_Radar_CVPRW_2020_paper.html)\n* 2021-[Perception Through 2D-MIMO FMCW Automotive Radar Under Adverse Weather](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.01639)\n* 2021-[Deep-Learning Based Decentralized Frame-to-Frame Trajectory Prediction Over Binary Range-Angle Maps for Automotive Radars]()\n\n#### Range Doppler Map\n* 2018-[Single-Frame Vulnerable Road Users Classification with a 77 GHz FMCW Radar Sensor and a Convolutional Neural Network](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8448126)\n* 2018-[Moving Target Classification in Automotive Radar Systems Using Convolutional Recurrent Neural Networks](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8553185)\n* 2019-[A Study on Radar Target Detection Based on Deep Neural Networks](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8629967)\n* 2020-[Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9054511)\n* 2021-[Detecting High-Speed Maneuvering Targets by Exploiting Range-Doppler Relationship for LFM Radar](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9347707)\n* 2021-[DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564526)\n\n#### ROD2021 Challenge Paper\n* 2021-[Radar Object Detection Using Data Merging, Enhancement and Fusion](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463653)\n* 2021-[Squeeze-and-Excitation network-Based Radar Object Detection with Weighted Location Fusion](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3460426.3463654)\n* 2021-[Scene-aware Learning Network for Radar Object Detection](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463655)\n* 2021-[DANet: Dimension Apart Network for Radar Object Detection](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463656)\n* 2021-[Efficient-ROD: Efficient Radar Object Detection based on Densely Connected Residual Network](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3460426.3463657)\n* 2021-[ROD2021 Challenge: A Summary for Radar Object Detection Challenge for Autonomous Driving Applications](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463658)\n\n---\n\n## Sensor Fusion\n* 2024-RCBEVDet: Radar-camera Fusion in Bird’s Eye View for 3D Object Detection __`CVPR`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15883)\n* 2024-Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions __`CVPR`__;\n* 2024-CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation __`CVPR`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhao_CRKD_Enhanced_Camera-Radar_Object_Detection_with_Cross-modality_Knowledge_Distillation_CVPR_2024_paper.html)\n* 2024-LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection __`ICRA`__; __`nuScenes`__ [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.11735)\n* 2023-CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception __`nuScenes`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FKim_CRN_Camera_Radar_Net_for_Accurate_Robust_Efficient_3D_Perception_ICCV_2023_paper.html)\n* 2023-LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion __`VOD`__; __`CVPR`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.00724)\n* 2023-RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.06108)\n* 2023-Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection __`CVPR`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FWang_Bi-LRFusion_Bi-Directional_LiDAR-Radar_Fusion_for_3D_Dynamic_Object_Detection_CVPR_2023_paper.html)\n* 2023-MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion __`nuScenes`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10511)\n* 2022-RADIANT: Radar-Image Association Network for 3D Object Detection __`AAAI`__; __`nuScenes`__; [Paper](http:\u002F\u002Fcvlab.cse.msu.edu\u002Fpdfs\u002FLong_Kumar_Morris_Liu_Castro_Chakravarty_AAAI2023.pdf)\n* 2022-Detecting darting out pedestrians with occlusion aware sensor fusion of radar and stereo camera ; __`TIV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9941368)\n* 2022-CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection; __`ECCV`__; __`RADIATE`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.09267)\n* DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars __`nuScenes`__ ; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.12729)\n* 2022-CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer __`nuScenes`__ ; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.06535)\n* 2022- Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection  __`BEVFeature`__; __`nuScenes`__ ; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12079)\n* 2022- RadSegNet: A Reliable Approach to Radar Camera Fusion __`RADIATE`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.03849)\n* 2022- HRFuser: A Multi-resolution Sensor Fusion\nArchitecture for 2D Object Detection __`CrossAttention`__; __`nuScenes`__ ; __`DENSE`__ ; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.15157); [Code](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.15157)\n* 2022-A Simple Baseline for BEV Perception Without LiDAR  __`BEVFeature`__; __`nuScenes`__ ;[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07959)\n* 2022-Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection __`CVPR`__; __`TeacherStudent`__ ;__`Oxford_Foggy`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html) \n* 2022-Global-Local Feature Enhancement Network for Robust Object Detection using mmWave Radar and Camera __`ICASSP`__; __`ROI+Transformer`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9746764)\n* 2022-Radar Guided Dynamic Visual Attention for Resource-Efficient RGB Object Detection __`IJCNN`__; __`RadarROI`__; __`nuScenes`__ ;  [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.01772)\n* 2021-RVDet:Feature-level Fusion of Radar and Camera for Object Detection __`ITSC`__; __`BEVFeature`__ ; __`Fisheye_Camera`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564627)\n* 2021-Radar Camera Fusion via Representation Learning in Autonomous Driving; __`CVPRW`__; __`VisualSemantics`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021W\u002FMULA\u002Fhtml\u002FDong_Radar_Camera_Fusion_via_Representation_Learning_in_Autonomous_Driving_CVPRW_2021_paper.html); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kBkdw4qFznU&t=5s)\n* 2021-Robust Small Object Detection on the Water Surface through Fusion of Camera and MillimeterWave Radar __`ICCV`__; __`Attention`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fhtml\u002FCheng_Robust_Small_Object_Detection_on_the_Water_Surface_Through_Fusion_ICCV_2021_paper.html)\n* 2021-Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals __`CVPR`__; __`Attention`__; __`Oxford_Foggy`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fhtml\u002FQian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html); [Code](https:\u002F\u002Fgithub.com\u002Fqiank10\u002FMVDNet)\n* 2021-CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking __`IV`__; __`CenterFusion+Track`__; __`nuScenes`__;  [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.05150); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_vuO19L6L0Q)\n\n\n* 2019-[Distant Vehicle Detection Using Radar and Vision](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8794312)\n* 2019-[RVNet: Deep Sensor Fusion of Monocular Camera and Radar for Image-based Obstacle Detection in Challenging Environments](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-34879-3_27)\n* 2019-[Radar and Camera Early Fusion for Vehicle Detection in Advanced Driver Assistance Systems](https:\u002F\u002Fml4ad.github.io\u002Ffiles\u002Fpapers\u002FRadar%20and%20Camera%20Early%20Fusion%20for%20Vehicle%20Detection%20in%20Advanced%20Driver%20Assistance%20Systems.pdf)\n* 2019-[A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8916629)\n* 2019-[Deep Learning Based 3D Object Detection for Automotive Radar and Camera](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8904867)\n* 2020-[YOdar Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03320v2)\n* 2020-[Radar+ RGB Fusion For Robust Object Detection In Autonomous Vehicle](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9191046)\n* 2020-[Low-level Sensor Fusion for 3D Vehicle Detection using Radar Range-Azimuth Heatmap and Monocular Image](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2020\u002Fhtml\u002FKim_Low-level_Sensor_Fusion_Network_for_3D_Vehicle_Detection_using_Radar_ACCV_2020_paper.html)\n* 2020-[Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F4\u002F956)\n\n\n\n* 2019-[RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8803392)\n* 2020-[CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.04841)\n* 2020-[Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08428)\n* 2020-[GRIF Net: Gated Region of Interest Fusion Network for Robust 3D Object Detection from Radar Point Cloud and Monocular Image](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9341177)\n* 2021-[milliEye: A Lightweight mmWave Radar and Camera Fusion System for Robust Object Detection](http:\u002F\u002Faiot.ie.cuhk.edu.hk\u002Fpapers\u002FmilliEye.pdf)\n\n* 2021-[3D Detection and Tracking for On-road Vehicles with a Monovision Camera and Dual Low-cost 4D mmWave Radars](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564904)\n\n* 2011-[Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F11\u002F9\u002F8992)\n* 2017-[Comparative Analysis of RADAR- IR Sensor Fusion Methods for Object Detection](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8204237?casa_token=Iclafffn7ZEAAAAA:0zfHdvHe2VXd3mQOW_AMjexp-fL4zfzyTZ7CKesXw7jnKEuWe0Ty-akJBW4HYg8pkfJtzfPhz5k)\n* 2019-[People Tracking by Cooperative Fusion of RADAR and Camera Sensors](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8917238)\n* 2019-[A DNN-LSTM based Target Tracking Approach using mmWave Radar and Camera Sensor Fusion](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9058168)\n* 2020-[Autonomous Obstacle Avoidance for UAV based on Fusion of Radar and Monocular Camera](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341432?casa_token=hZJ5haSOVqEAAAAA:UoczK2JQxnrkU_rdXKERsm6oVDtLwtembw1iB-dBrrKuZqfbDjgSA4phgCNRI0H-cxGuj_d8NqY)\n* 2019-[Extending Reliability of mmWave Radar Trackingand Detection via Fusion With Camera](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8844649)\n* 2019-[People Tracking by Cooperative Fusion ofRADAR and Camera Sensors](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917238)\n* 2019-[TargetDetection Algorithm Based on MMW Radar and Camera Fusion](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917504)\n* 2019-[A DNN-LSTM based Target Tracking Approachusing mmWave Radar and Camera Sensor Fusion](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9058168?casa_token=-51KkM4RDJUAAAAA:DiM3jG_heIcHkxgsAmrE5ewfVRSrqbp24ChSzRAKYY-nXboD9KZKOp0G1Jl4B0PKi53UnhBfZCI)\n* 2020-[A Roadside Camera-Radar Sensing Fusion System for Intelligent Transportation](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9337488?casa_token=4Wws9Vyc4UcAAAAA:ktkw199jc7Wtlk2CjHDxOPMQnPCTWINWTZUqEuuTFuL3TsC-P3_U7wqSEQfOPNj72oNt98KFATY)\n* 2020-[Cooperative Multi-Sensor Tracking of VulnerableRoad Users in the Presence of Missing Detections](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F17\u002F4817)\n* 2020-[Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera ](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F21\u002F4\u002F1116)\n\n* 2017-[Vehicle Tracking Using Extended Object Methods: An Approach for Fusing Radar and Laser](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7989029)\n* 2019-[Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8726801)\n* 2019-[Learning to see through haze: Radar-based Human Detection for Adverse Weather Conditions](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8870954)\n* 2020-[LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00731)\n* 2020-[RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.14366)\n\n* 2016-[Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7283636)\n* 2020-[High Dimensional Frustum PointNet for 3D Object Detection from Camera, LiDAR, and Radar](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9304655)\n* 2020-[Seeing Through FogWithout Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9304655)\n* 2021-[Radar Voxel Fusion for 3D Object Detection](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F12\u002F5598)\n\n---\n\n## Weakly Supervised\n* 2022-A Novel Radar Point Cloud Generation Method for Robot Environment Perception [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9823311)\n* 2022-Look, Radiate, and Learn: Self-supervised Localisation via Radio-Visual Correspondence __`Arxiv`__; __`Simulation`__; __`SpatialContrastive`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.06424)\n* 2021-R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes __`3DIMPVT`__; __`nuScenes`__; __`SSL`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.04814)\n* 2021- RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization __`IJSTSP`__; __`CRUW`__; __`ConfMap`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9353210); [Code](https:\u002F\u002Fgithub.com\u002Fyizhou-wang\u002FRODNet); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UZbxI4o2-7g)\n* 2020- RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar __`IV`__; __`Oxford`__; __`PoseChain`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9304674)\n* 2020-Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications __`TVT`__; __`Velocity`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.12809)\n* 2020-Weakly Supervised Deep Learning Method for Vulnerable Road User Detection in FMCW Radar  __`ITSC`__; __`Tracking`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9294399)\n* 2020- Radar as a Teacher: Weakly Supervised Vehicle Detection using Radar Labels __`ICRA`__; __`CoTeaching`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9196855)\n\n---\n\n## Depth Estimation\n* 2023-Depth Estimation From Camera Image and mmWave Radar Point Cloud __`CVPR`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FSingh_Depth_Estimation_From_Camera_Image_and_mmWave_Radar_Point_Cloud_CVPR_2023_paper.html)\n* 2022-RVMDE: Radar Validated Monocular Depth Estimation for Robotics __`Arxiv`__; __`nuScenes`__ [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05265); [Code](https:\u002F\u002Fgithub.com\u002FMI-Hussain\u002FRVMDE)\n* 2021-Semantic-guided radar-vision fusion for depth estimation and object detection __`BMVC`__; __`nuScenes`__; __`SemanticJoint`__; [Paper](https:\u002F\u002Fbiblio.ugent.be\u002Fpublication\u002F8713974) \n* 2021-Depth estimation from monocular images and sparse radar using deep ordinal regression network __`ICIP`__; __`nuScenes`__; __`DORN`__ ; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9506550); [Code](https:\u002F\u002Fgithub.com\u002Flochenchou\u002FDORN)\n* 2021-R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes __`3DIMPVT`__; __`nuScenes`__; __`SSL`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.04814)\n* 2021-Radar-Camera Pixel Depth Association for Depth Completion __`CVPR`__; __`nuScenes`__; __`MER`__ ;  [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fhtml\u002FLong_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.html); [Code](https:\u002F\u002Fgithub.com\u002Flongyunf\u002Frc-pda)\n* 2020-Depth Estimation from Monocular Images and Sparse Radar Data __`IROS`__; __`nuScenes`__; __`TwoStage`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9340998);  [Code](https:\u002F\u002Fgithub.com\u002Fbrade31919\u002Fradar_depth)\n* 2020-Camera-Radar Fusion for 3-D Depth Reconstruction __`IV`__; __`RadarRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9304559); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T9c75fmmxyQ)\n\n---\n\n## Ego Motion Estimation\n* 2022-A Credible and Robust Approach to Ego-Motion Estimation Using an Automotive Radar __`RAL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9743799)\n* 2021-3D ego-Motion Estimation Using low-Cost mmWave Radars via Radar Velocity Factor for Pose-Graph SLAM __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9495184)\n* 2020-milliEgo: Single-chip mmWave Aided Egomotion Estimation with Deep Sensor Fusion __`SenSys`__; [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3384419.3430776); [Code](https:\u002F\u002Fgithub.com\u002FChristopherLu\u002FmilliEgo)\n* 2020-Radar-Inertial Ego-Velocity Estimation for Visually Degraded Environments __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9196666)\n* 2018-Precise Ego-Motion Estimation with Millimeter-Wave Radar under Diverse and Challenging Conditions __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8460687)\n* 2014-Instantaneous Ego-Motion Estimation using Multiple Doppler Radars __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6907064)\n\n---\n\n## Velocity Estimation\n* 2023-Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision  __`CVPR`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.00462)\n* 2022-Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks __`nuScenes`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03146)\n* 2022-Self-Supervised Scene Flow Estimation with 4D Automotive Radar __`RAL`__; __`4DRadar`__; __`FlowNet`__; [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.01137); [Code](https:\u002F\u002Fgithub.com\u002FToytiny\u002FRaFlow)\n* 2021-Full-Velocity Radar Returns by Radar-Camera Fusion __`ICCV`__; __`nuScenes`__; __`OpticalFlow`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fhtml\u002FLong_Full-Velocity_Radar_Returns_by_Radar-Camera_Fusion_ICCV_2021_paper.html)\n* 2021-3D Radar Velocity Maps for Uncertain Dynamic Environments __`IROS`__; __`Bayesian`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636019); [Code](https:\u002F\u002Fgithub.com\u002FRansML\u002FBDF)\n* 2020-An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking  __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341127)\n* 2018- Instantaneous Actual Motion Estimation with a Single High-Resolution Radar Sensor __`Nonlinear`__ [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8443553)\n* 2014-Instantaneous Full-Motion Estimation of Arbitrary Objects using Dual Doppler Radar  __`DualRadar`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856449)\n* 2013-Instantaneous lateral velocity estimation of a vehicle using Doppler radar __`MultiPts`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6641086)\n\n\n---\n\n## Tracking\n\n### Neural Network\n* 2022- Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time\n [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.06126)\n* 2022-Exploiting Temporal Relations on Radar Perception for Autonomous Driving __`CVPR`__; __`Oxford`__; __`Attention`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html)\n* 2021-End-to-End On-Line Multi-object Tracking on Sparse Point Clouds Using Recurrent Convolutional Networks [Paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-86380-7_33)\n* 2021-CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking __`IV`__; __`CenterFusion+Track`__; __`nuScenes`__;  [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.05150); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_vuO19L6L0Q)\n\n### Bayesian Filtering\n* 2021-Road-Map Aided GM-PHD Filter for Multi-Vehicle Tracking with Automotive Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9403944)\n* 2021-Bayesradar: Bayesian Metric-Kalman Filter Learning for Improved and Reliable Radar Target Classification [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9596290)\n* 2021-Extended Object Tracking assisted Adaptive Multi-Hypothesis Clustering for Radar in Autonomous Driving Domain [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9466233)\n* 2021-A Graph-Based Track-Before-Detect Algorithm for Automotive Radar Target Detection [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9276431)\n* 2020-BAAS: Bayesian Tracking and Fusion Assisted Object Annotation of Radar Sensor Data for Artificial Intelligence Application __`RadarConf`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9266698)\n* 2020-An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking  __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341127)\n* 2021-Automotive Radar-Based Vehicle Tracking Using Data-Region Association __`TITS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9525315)\n* 2020-Extended Object Tracking Using Spatially Resolved Micro-Doppler Signatures __`TIV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9247291)\n* 2019-Extended Object Tracking assisted Adaptive Clustering for Radar in Autonomous Driving Applications [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8916658)\n* 2018- Rfcm for Data Association and Multitarget Tracking Using 3D Radar __`ICASSP`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8461917)\n* 2018-Classification Assisted Tracking for Autonomous Driving Domain [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8547138)\n* 2015-Tracking of Extended Objects with High-Resolution Doppler Radar __`TITS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7355362)\n* 2010-Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter __`TSP`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5677613)\n\n\n### Modelling\n* 2022-A Data-driven Approach for Stochastic Modeling of Automotive Radar Detections for Extended Objects __`GeMic`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9783497)\n* 2021-A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving __`IV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564521)\n* 2021-Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar __`JSTSP`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9351598)\n* 2020-Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar __`ICASSP`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9054614)\n\n\n\n---\n\n## Prediction\n* 2021-Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9520242)\n* 2021-Deep-Learning Based Decentralized Frame-to-Frame Trajectory Prediction Over Binary Range-Angle Maps for Automotive Radars __`TVT`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9437958)\n* 2020-LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00731)\n* 2020-FISHING Net: Future Inference of Semantic Heatmaps in Grids [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09917)\n\n---\n\n## Occupancy Grid Map\n* 2022-Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities __`RAL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9689995)\n* 2020-See Through Smoke: Robust Indoor Mapping with Low-cost\nmmWave Radar __`MobiSys`__; [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3386901.3388945)\n* 2019-Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation __`ICCV`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCVW_2019\u002Fhtml\u002FCVRSUAD\u002FSless_Road_Scene_Understanding_by_Occupancy_Grid_Learning_from_Sparse_Radar_ICCVW_2019_paper.html)\n* 2019- Probably Unknown: Deep Inverse Sensor Modelling Radar __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8793263)\n* 2019-Occupancy Grids Generation Using Deep Radar Network for Autonomous Driving __`ITSC`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8916897)\n* 2015-Automotive Radar Gridmap Representations [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7117922)\n\n### Point Cloud Map\n* 2022-High Resolution Point Clouds from mmWave Radar [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.09273)\n* 2020-Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9340856)\n\n\n---\n\n## Open Space Segmentation\n* 2022-Raw High-Definition Radar for Multi-Task Learning __`CVPR`__; __`Dataset`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FRebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html)\n* 2022-Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.01442)\n* 2022-Drivable Region Estimation for Self-Driving Vehicles Using Radar __`TVT`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9740418)\n* 2021-PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03387)\n* 2020-Deep Open Space Segmentation using Automotive Radar __`Dataset`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9299052)\n* 2018-High Resolution Radar-based Occupancy Grid Mapping and Free Space Detection [Paper](https:\u002F\u002Fwww.scitepress.org\u002Fpapers\u002F2018\u002F66673\u002F)\n\n---\n\n## Scene Understanding\n* 2020-Semantic Segmentation on 3D Occupancy Grids for Automotive Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9229096)\n* 2020-Statistical Image Segmentation and Region Classification Approaches for Automotive Radar __`EuRAD`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9337399)\n* 2019-Scene Understanding With Automotive Radar __`TIV`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8911477)\n* 2018-Semantic Segmentation on Radar Point Clouds __`FUSION`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8455344)\n\n---\n\n## Place Recognition\n* 2024-TransLoc4D: Transformer-based 4D-Radar Place Recognition __`CVPR`__\n* 2022-AutoPlace: Robust Place Recognition with Single-chip Automotive Radar __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811869); [Code](https:\u002F\u002Fgithub.com\u002Framdrop\u002FAutoPlace)\n* 2021-Contrastive Learning for Unsupervised Radar Place Recognition [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9659335)\n* 2021-Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning [Paper](https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffrobt.2021.661199\u002Ffull)\n* 2021-Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06703)\n* 2020-Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9109951)\n* 2020-MulRan Multimodal Range Dataset for Urban Place Recognition  __`ICRA`__; __`Dataset`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9197298)\n\n---\n\n## Odometry and SLAM\n### Odometry\n* 2022-Radar Odometry on SE(3) with Constant Acceleration Motion Prior and Polar Measurement Model [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.05956)\n* 2022-Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812063) __`ICRA`__; [Code](https:\u002F\u002Fgithub.com\u002Fapplied-ai-lab\u002Ff-mbym)\n* 2021-Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.14152)\n* 2021-Radar Odometry on SE(3) With Constant Velocity Motion Prior __`RAL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9463737)\n* 2022-What Goes Around: Leveraging a Constant-curvature Motion Constraint in Radar Odometry __`RAL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9808131)\n* 2021-Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation __`RAL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9327473); [Code](https:\u002F\u002Fgithub.com\u002Fkeenan-burnett\u002Fyeti_radar_odometry)\n* 2021-A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9561413)\n* 2021-BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.09669)\n* 2021-CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636253)\n* 2021-Continuous-time Radar-inertial Odometry for Automotive Radars __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636014)\n* 2021-Oriented surface points for efficient and accurate radar odometry [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.09994)\n* 2020-PhaRaO: Direct Radar Odometry using Phase Correlation __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9197231)\n* 2019-Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information __`CoRL`__; [Paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv100\u002Fbarnes20a)\n\n### SLAM\n* 2022-CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy __`RAS`__;  [Paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0921889022000768)\n* 2022-Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization [Paper](http:\u002F\u002F128.84.4.18\u002Fabs\u002F2203.10174)\n* 2021-SERALOC: SLAM on semantically annotated radar point-clouds __`ITSC`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564693)\n* 2021-RaLL: End-to-End Radar Localization on Lidar Map Using Differentiable Measurement Model __`TITS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9370010)\n* 2021-Improved Radar Localization on Lidar Maps Using Shared Embedding [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.10000)\n* 2021-Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions __`RAL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9362209)\n* 2021-RadarLoc: Learning to Relocalize in FMCW Radar __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9560858)\n* 2021-Radar SLAM: A Robust SLAM System for All Weather Conditions [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05347)\n* 2020-RadarSLAM: Radar based Large-Scale SLAM in All Weathers __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341287)\n* 2020-Self-Supervised Localisation between Range Sensors and Overhead Imagery [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.02108)\n* 2020-RSL-Net: Localising in Satellite Images From a Radar on the Ground __`RAL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8957240)\n* 2020-Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9196835)\n* 2020-Radar-on-Lidar: metric radar localization on prior lidar maps [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9303291)\n* 2020-kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation [Paper](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F21\u002F6002)\n* 2020-Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning __`ICRA`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9196682)\n* 2020-A Scalable Framework for Robust Vehicle State Estimation with a Fusion of a Low-Cost IMU, the GNSS, Radar, a Camera and Lidar __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341419)\n* 2005-An Augmented State SLAM formulation for Multiple Line-of-Sight Features with Millimetre Wave RADAR __`IROS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1545232)\n\n\n---\n\n## Automotive SAR\n* 2022-Synthetic Aperture Radar Imaging of Moving Targets for Automotive Applications __`EuRAD`__; __`Bosch`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784564)\n* 2022-Performance Analysis of Automotive SAR With Radar Based Motion Estimation __`GM`__;  [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.10406)\n* 2022-Residual Motion Compensation in Automotive MIMO SAR Imaging __`RadarConf`__; __`Huawei`__;  [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9764310)\n* 2022-A Quick and Dirty processor for automotive forward SAR imaging __`RadarConf`__; __`Huawei`__;  [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9764234)\n* 2021-Cooperative Synthetic Aperture Radar in an Urban Connected Car Scenario __`Huawei`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9376348)\n* 2021-Navigation-Aided Automotive SAR for High-Resolution Imaging of Driving Environments  __`Huawei`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9363205)\n* 2021-Imaging radar for automated driving functions __`Continental`__; [Paper](https:\u002F\u002Fwww.cambridge.org\u002Fcore\u002Fjournals\u002Finternational-journal-of-microwave-and-wireless-technologies\u002Farticle\u002Fimaging-radar-for-automated-driving-functions\u002F81B92D1CCF86309E8A354783A343861E)\n* 2021-MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving __`TVT`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9465646)\n* 2020-3D Point Cloud Generation with Millimeter-Wave Radar [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3432221)\n* 2020-High Resolution Radar Dataset for Semi-Supervised Learning of Dynamic\nObjects __`CVPRW`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2020\u002Fhtml\u002Fw6\u002FMostajabi_High-Resolution_Radar_Dataset_for_Semi-Supervised_Learning_of_Dynamic_Objects_CVPRW_2020_paper.html)\n\n### ISAR\n* 2022-Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9695280)\n* 2021-Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9513303\u002F)\n\n---\n \n## Human Activity Recognition\n\n### Pointcloud\n* 2022-Cross Vision-RF Gait Re-identification with Low-cost RGB-D\nCameras and mmWave Radars [Paper](https:\u002F\u002Fwww.research.ed.ac.uk\u002Fen\u002Fpublications\u002Fcross-vision-rf-gait-re-identification-with-low-cost-rgb-d-camera)\n* 2022-Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Sparse Point Clouds __`TMC`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9720163)\n* 2021-m-Activity Accurate and Real-Time Human Activity Recognition Via Millimeter Wave Radar __`ICASSP`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9414686)\n* 2020-Real-time Arm Gesture Recognition in Smart Home Scenarios via MillimeterWave Sensing [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3432235)\n* 2019-RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3349624.3356768)\n\n### Micro-Doppler\n* 2022-Attention-Based Dual-Stream Vision Transformer for Radar Gait Recognition __`ICASSP`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9746565)\n* 2021-Human Motion Recognition With Limited Radar Micro-Doppler Signatures __`TGRS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9222330)\n* 2018-Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks __`TGRS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8307105)\n\n### RD\n* 2021-RadarNet: Efficient Gesture Recognition Technique Utilizing a Miniature Radar Sensor __`CHI`__; [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3411764.3445367)\n* 2016-Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F2984511.2984565); [Video](https:\u002F\u002Fyoutu.be\u002FZSkl9zoNZRY)\n* 2015-Short-Range FMCW Monopulse Radar for Hand-Gesture Sensing __`RadarConf`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7131232)\n\n\n### Domain Adaption\n* 2022-Unsupervised Learning for Human Sensing Using Radio Signals __`WACV`__; [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2022\u002Fhtml\u002FLi_Unsupervised_Learning_for_Human_Sensing_Using_Radio_Signals_WACV_2022_paper.html)\n* 2022-Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.04588)\n* 2022-mTransSee: Enabling Environment-Independent mmWave Sensing Based Gesture Recognition via Transfer Learning [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3517231)\n* 2022-Few-Shot User-Definable Radar-Based Hand Gesture Recognition at the Edge [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9722880)\n* 2021-Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.06195)\n* 2021-Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures __`TGRS`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9467531)\n* 2021-Supervised Domain Adaptation for Few-Shot Radar-Based Human Activity Recognition [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9558812)\n* 2021-Towards Cross-Environment Human Activity Recognition Based on Radar Without Source Data  [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9551721)\n* 2021-Continual Learning of Micro-Doppler Signature-Based Human Activity Classification __`GRSL`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9319850)\n\n### Distributed\n* 2022-Exploiting Radar Data Domains for Classification with Spatially Distributed Nodes [Paper](https:\u002F\u002Fresearch.tudelft.nl\u002Fen\u002Fpublications\u002Fexploiting-radar-data-domains-for-classification-with-spatially-d)\n\n\n\n---\n\n## Radar-Audio\n### Speech Recovery\n* 2022-Wavesdropper: Through-wallWord Detection of Human Speech via\nCommercial mmWave Devices [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534592)\n* 2022-Radio2Speech: High Quality Speech Recovery from Radio Frequency Signals [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.11066)\n* 2021-mmPhone: Acoustic Eavesdropping on Loudspeakers via mmWave-characterized Piezoelectric Effect __`INFOCOM`__; [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9796806)\n\n### Vocal Chord\n* 2022-Multi-target Time-Varying Vocal Folds Vibration\nDetection Using MIMO FMCW Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9765794)\n* 2021-VocalPrint: A mmWave-based Unmediated Vocal Sensing\nSystem for Secure Authentication [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9444641)\n\n### Separation\n* 2022-RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.07092)\n* 2021-Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals __`SenSys`__; [Paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3485730.3485945)\n\n\n---\n\n## Weather Effects\n\n### Effect\n* The Impact of Adverse Weather Conditions on Autonomous Vehicles: How Rain, Snow, Fog, and Hail Affect the Performance of a Self-Driving Car [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8666747)\n* Seeing through dust and water vapor: Millimeter wave radar sensors for mining applications [Paper](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1002\u002Frob.20166)\n* Analysis of rain clutter detections in commercial 77 GHz automotive radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8249138)\n* The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review [Paper](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F22\u002F6532)\n* Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions [Paper](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F9\u002F11\u002F2341)\n* Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9307324)\n* Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions [Paper](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F9\u002F11\u002F2341)\n* What Happens for a ToF LiDAR in Fog? [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9121741)\n\n### Datasets\n* Oxford Foggy [Code]((https:\u002F\u002Fgithub.com\u002Fqiank10\u002FMVDNet))\n* DENSE \n* RADIATE \n* Boreas\n* K-Radar\n \u003C\u002Fbr>See the dataset section for details.\n\n###  Methods\n* 2022-Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html)\n* 2021-Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fhtml\u002FQian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html)\t\n* 2020-Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FBijelic_Seeing_Through_Fog_Without_Seeing_Fog_Deep_Multimodal_Sensor_Fusion_CVPR_2020_paper.html); [Code](); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HPT4nsCkT5Q)\n* 2020-Through Fog High Resolution Imaging Using Millimeter Wave Radar [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FGuan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html); [Code](https:\u002F\u002Fgithub.com\u002FJaydenG1019\u002FHawkEye-Data-Code); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HPT4nsCkT5Q)\n\n---\n\n## Multi-Path Effect\n\n### Dataset\n* 2021-The Radar Ghost Dataset – An Evaluation of Ghost Objects in Automotive Radar Data [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636338); [Code](https:\u002F\u002Fgithub.com\u002Fflkraus\u002Fghosts)\n\n### Methods\n* 2021-Anomaly Detection in Radar Data Using PointNets [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564730)\n* 2021-Fast Rule-Based Clutter Detection in Automotive Radar Data [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564776)\n* 2021-Radar Ghost Target Detection via Multimodal Transformers [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9497756)\n* 2021-Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9413247)\n* 2020- Using Machine Learning to Detect Ghost Images in Automotive Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9294631)\n* 2020-Seeing Around Street Corners Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar [Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FScheiner_Seeing_Around_Street_Corners_Non-Line-of-Sight_Detection_and_Tracking_In-the-Wild_Using_CVPR_2020_paper.html)\n* 2019-Identification of Ghost Moving Detections in\nAutomotive Scenarios with Deep Learning [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8726704)\n* 2018-Automotive Radar Multipath Propagation in Uncertain Environments [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8570016)\n\n\n\n---\n\n## Mutual Interference\n### Methods\n* 2022-A Two-stage DNN Model with Mask-gated Convolution for Automotive Radar Interference Detection and Mitigation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9770068)\n* 2021-Resource-Efficient Deep Neural Networks for Automotive Radar Interference Mitigation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9364355)\n* 2021-A DNN Autoencoder for Automotive Radar Interference Mitigation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9413619)\n* 2021-CFAR-Based Interference Mitigation for FMCW Automotive Radar Systems [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9541302)\n* 2021-Mutual Interference Suppression Using Wavelet Denoising in Automotive FMCW Radar Systems [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8946905)\n* 2020-Interference Characterization in FMCW radars [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9266283)\n* 2020-Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9114627); [Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DJY9Zk1p9-g)\n* 2019-Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9011164)\n* 2018-A Deep Learning Approach for Automotive Radar Interference Mitigation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8690848)\n### Reviews\n* 2022-Interference Suppression Using Deep Learning: Current Approaches and Open Challenges [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9802083)\n* 2020-Radar Interference Mitigation for Automated Driving: Exploring proactive strategies [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9127843)\n* 2020-Interference in Automotive Radar Systems Characteristics, mitigation techniques, and current and future research [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828037)\n\n## Range and Doppler Cell Migration\n* 2021-Doppler–Range Processing for Enhanced High-Speed Moving Target Detection Using LFMCW Automotive Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9506841)\n* 2019-Range and Doppler Cell Migration in Wideband Automotive Radar [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8695853)\n\n## Tx-Rx Leakage\n* 2022-Mitigation of Leakage and Stationary Clutters in Short-Range FMCW Radar With Hybrid Analog and Digital Compensation Technique [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9583883)\n* 2019-The Effect of Antenna Mutual Coupling on MIMO Radar System Performance [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8581509)\n\n## Imperfect Waveform Separation\n* 2020-Slow-Time MIMO-FMCW Automotive Radar Detection with Imperfect Waveform Separation [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9053892)\n","\u003Cdiv align=\"center\">\n    \u003Cimg class=\"aligncenter\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FZHOUYI1023_awesome-radar-perception_readme_8ddb0a3a085d.png\"\u002F>\n\u003C\u002Fdiv>\n\u003Cbr>\n\n\n雷达数据集、目标检测、跟踪及融合的精选列表。\u003Cbr>持续更新中。\u003Cbr>作者：周毅\u003Cbr>联系方式：zhouyi1023@tju.edu.cn\n\n\n🚩我发表了一篇关于雷达感知的综述论文，请参阅下方链接。该论文为开放获取。如果您认为其中的内容有用，请在您的工作中引用本文。我将持续更新此仓库，以收录雷达感知领域的最新研究成果。\n\n## [面向自动驾驶的深度雷达感知：数据集、方法与挑战](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F22\u002F11\u002F4208)\n## 与本文相关的41页幻灯片：[链接](https:\u002F\u002Fwww.slideshare.net\u002FYiZhou66\u002Fslidesdeepradarperceptionforautonomousdrivingpdf)；[中国大陆访问链接](https:\u002F\u002Fwww.aliyundrive.com\u002Fs\u002FCZ3SKqY3U4w) \n\n---\n\n## 目录\n概述\n- [综述论文](#Review-Papers)\n- [研讨会和工作坊](#Seminars-and-Workshops)\n\n数据视角：\n- [雷达数据集](#Radar-Datasets)\n- [雷达特征](#Radar-Signature)\n- [标定](#Calibration)\n- [标注](#Labelling)\n- [数据增强](#Data-Augmentation)\n- [仿真](#Simulator)\n- [生成模型](#Generative-Model)\n- [测试](#Testing)\n\n信号处理：\n- [雷达工具箱](#Radar-Toolbox)\n- [MIMO标定](#MIMO-Calibration)\n- [检测器](#Detector)\n- [超分辨率](#Super-Resolution)\n- [聚类](#Clustering)\n- [去噪](#Denoising)\n\n\n应用：\n- [TI参考设计](#TI-Reference-Designs)\n- [自车运动估计](#Ego-Motion-Estimation)\n- [速度估计](#Velocity-Estimation)\n- [深度估计](#Depth-Estimation)\n- [目标检测](#Object-Detection)\n- [传感器融合](#Sensor-Fusion)\n- [弱监督](#Weakly-Supervised)\n- [跟踪](#Tracking)\n- [预测](#Prediction)\n- [占用栅格地图](#Occupancy-Grid-Map)\n- [空旷区域分割](#Open-Space-Segmentation)\n- [场景理解（静态分割）](#Scene-Understanding)\n- [地点识别](#Place-Recognition)\n- [里程计与SLAM](#Odometry-and-SLAM)\n- [车载合成孔径雷达](#Automotive-SAR)\n- [人体活动识别](#Human-Activity-Recognition)\n- [雷达-音频](#Radar-Audio)\n\n挑战：\n- [天气影响](#Weather-Effects)\n- [多径效应](#Multi-Path-Effect)\n- [相互干扰](#Mutual-Interference)\n- [单元迁移](#Range-and-Doppler-Cell-Migration)\n- [收发泄漏](#Tx-Rx-Leakage)\n- [波形分离不完善](#Imperfect-Waveform-Separation)\n\u003Cbr>\n\n---\n\n## 雷达数据集\n在我的[综述论文](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F22\u002F11\u002F4208)中，有一张表格提供了更多细节。\n\n\n### 自动驾驶用传统雷达数据集\n| 数据集 | 雷达类型 | 数据类型 | 标注 | 链接 |\n| ---- |----| ---- | ---- | ---- |\n| nuScenes | Continental ARS408 ×5 | 稀疏点云 | 3D边界框，轨迹ID | [官网](https:\u002F\u002Fwww.nuscenes.org\u002F) |\n| DENSE| 77GHz长距雷达 | 稀疏点云 | 3D边界框 |[官网](https:\u002F\u002Fwww.uni-ulm.de\u002Fen\u002Fin\u002Fdriveu\u002Fprojects\u002Fdense-datasets) |\n| PixSet| TI AWR1843| 稀疏点云 | 3D边界框，轨迹ID|  [官网](https:\u002F\u002Fleddartech.com\u002Fsolutions\u002Fleddar-pixset-dataset\u002F)|\n| Radar Scenes | 77GHz中距雷达×4 | 密集点云 |2D逐点标注，轨迹ID| [官网](https:\u002F\u002Fradar-scenes.com\u002F)|\n| Pointillism | 2 TI AWR 1443 | 点云 | 3D边界框 | [Github](https:\u002F\u002Fgithub.com\u002FKshitizbansal\u002Fpointillism-multi-radar-data) |\n| Zendar SAR | SAR | ADC、RD、点云| 移动物体的逐点掩码 |[Github](https:\u002F\u002Fgithub.com\u002FZendarInc\u002FZendarSDK) |\n| Cooperative Radars | 77GHz雷达×3 | 点云 | 来自GNSS-RTK的轨迹 | [官网](https:\u002F\u002Fieee-dataport.org\u002Fdocuments\u002Fradar-measurements-two-vehicles-three-cooperative-imaging-sensors) |\n| aiMotive| 77GHz LRR雷达×2（前后） | 点云 | 3D边界框，轨迹ID | [官网](https:\u002F\u002Fgithub.com\u002Faimotive\u002Faimotive_dataset)|\n\n\u003Cbr>评论：nuScenes、DENSE和Pixset主要用于传感器融合，但并未特别强调雷达的作用。Radar scenes提供了雷达点云的逐点标注，但没有其他模态数据。Pointillism使用了两部视场重叠的雷达。Zendar似乎已无法下载。AiMotive则专注于远距离的360度多传感器融合。\n\n\n### 检测用CFAR前数据集\n| 数据集 | 雷达类型 | 数据类型 | 标注 | 链接 |\n| ---- |----| ---- | ---- | ---- |\n| CRUW |  TI AWR1843 超短距 | RA | 物体级标注 |[官网](https:\u002F\u002Fwww.cruwdataset.org\u002Fhome)|\n| CARRADA | TI AWR1843 短距 | RA、RD、RAD | 逐点标注、2D边界框、掩膜 | [官网](https:\u002F\u002Farthurouaknine.github.io\u002Fcodeanddata\u002Fcarrada)|\n| RADDet | TI AWR1843 | RAD | 用于RAD张量的3D边界框 | [Github](https:\u002F\u002Fgithub.com\u002FZhangAoCanada\u002FRADDet) |\n| RaDICaL | TI IWR1443 | ADC | 2D边界框 | [官网](https:\u002F\u002Fpublish.illinois.edu\u002Fradicaldata\u002F)|\n| GhentVRU | TI AWR1243 短距 | RAD | VRU的分割掩膜 | [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9294399) |\n| RAMP-CNN | TI AWR 1843| ADC | 2D边界框 | [官网](https:\u002F\u002Fgithub.com\u002FXiangyu-Gao\u002FRaw_ADC_radar_dataset_for_automotive_object_detection) |\n\n\u003Cbr>评论：CARRADA是在干净场景下采集的，CRUW使用RA图，RADDet为RAD张量提供标注，RADICaL提供原始ADC数据和信号处理工具箱，GhentVRU可通过联系作者获取，ODA则针对无人机，提供事件相机数据。\n\n\n### 4D雷达数据集\n| 数据集 | 雷达类型 | 数据类型 | 标注 | 链接 |\n| ---- |----| ---- | ---- | ---- |\n| Astyx Hires2019 | Astyx 6455高分辨率中距 | 点云 | 3D边界框|[数据集](https:\u002F\u002Fgithub.com\u002Funder-the-radar\u002Fradar_dataset_astyx)|\n| View-of-Delft | ZF FRGen21短距 | 点云 | 3D边界框 |[官网](https:\u002F\u002Fintelligent-vehicles.org\u002Fdatasets\u002Fview-of-delft\u002F)|\n| RADIal | Valeo中距DDM | ADC、RAD、点云 | 车辆逐点标注；空旷区域掩码 |[Github](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FRADIal)|\n| TJ4DRadSet | Oculii Eagle长距 | 点云 | 3D边界框，轨迹ID | [Github](https:\u002F\u002Fgithub.com\u002FTJRadarLab\u002FTJ4DRadSet) |\n| K-Radar | Macnica RETINA | RAD |3D边界框，轨迹ID |[Github](https:\u002F\u002Fgithub.com\u002Fkaist-avelab\u002FK-Radar)；[OpenReview](https:\u002F\u002Fopenreview.net\u002Fforum?id=W_bsDmzwaZ7) |\n| ZF 4DRadar Dataset |  ZF FRGen21 4D | 3D | | 待定 [Github](https:\u002F\u002Fgithub.com\u002FZF4DRadSet\u002FZF-4DRadar-Dataset) |\n| ThermRad | Oculii Eagle | 点云 | 3D | 待定 | \n| MSC-RAD4R | Oculii Eagle | 点云 | SLAM | [官网](https:\u002F\u002Fmscrad4r.github.io\u002Fhome\u002F) | \n\n\u003Cbr>评论：Astyx规模较小，VoD侧重于VRU分类，RADIal的标注较为粗略但提供了原始数据，TJ4D以其长距离探测能力为特色，K-Radar则提供了RAD张量和3D标注。ZF 4DRadar Dataset目前尚未公开。\n\n### 具体任务\n| 数据集 | 雷达类型 | 任务 | 链接 |\n| ---- |----| ---- | ---- |\n| HawkEye | SAR | 静止车辆分类 | [网站](https:\u002F\u002Fjaydeng1019.github.io\u002FHawkEye\u002F)|\n| PREVENTION | Conti ARS308 + SRR208 x2 | 轨迹预测 | [网站](https:\u002F\u002Fprevention-dataset.uah.es\u002F)|\n| SCORP | 76GHz | 开放空间分割 | [网站](https:\u002F\u002Fsensorcortek.ai\u002Fpaper-and-datasets\u002F) |\n| Ghost | 77GHz 长距 *2  | 幽灵目标检测 | [Github](https:\u002F\u002Fgithub.com\u002Fflkraus\u002Fghosts) |\n| Solinteraction Data | Soli | 有形交互| [Github](https:\u002F\u002Fgithub.com\u002Ftcboy88\u002Fsolinteractiondata) |\n| GROUNDED | 地面穿透雷达 | 定位 | [网站](https:\u002F\u002Flgprdata.com\u002F)|\n|FloW Dataset | TI AWR1843 | 浮动垃圾检测 | [网站](http:\u002F\u002Forca-tech.cn\u002Fdatasets\u002FFloW\u002FIntroduction) |\n| OLIMP | UWB + Continental ARS404| 多传感器融合检测|[网站](https:\u002F\u002Fsites.google.com\u002Fview\u002Fihsen-alouani\u002Fdatasets)|\n| DeepSense 6G | 雷达+激光雷达+相机+GPS | 波束预测 | [网站](https:\u002F\u002Fdeepsense6g.net\u002F)|\n| CTA | 雷达+相机 | 干扰分析 | [网站](https:\u002F\u002Fedata.bham.ac.uk\u002F801\u002F) |\n| Radar^2 | TI AWR1843 | 盗听雷达检测 | [网站](https:\u002F\u002Fieee-dataport.org\u002Fdocuments\u002Fradar2) |\n| Darting Pedestrians dataset | ZF FRGen21 短程 | 突然横穿行人检测 | [网站](https:\u002F\u002Fintelligent-vehicles.org\u002Fdatasets\u002Fdarting-pedestrians-dataset\u002F) |\n| ODA | 24GHz | 无人机障碍物检测与避障 | [网站](https:\u002F\u002Fgithub.com\u002Ftudelft\u002FODA_Dataset) |\n| Scattering Dataset | 77GHz | | [网站](https:\u002F\u002Fwww.fzd-datasets.de\u002Frcs\u002F)|\n| Radar Clutter Dataset | 77GHz | 杂波检测 | [网站](https:\u002F\u002Fgithub.com\u002Fkopp-j\u002Fclutter-ds) |\n| Interference Dataset | 77GHz | 干扰 | [网站](https:\u002F\u002Fieee-dataport.org\u002Fdocuments\u002Fraw-adc-data-fmcw-radar-77-ghz-interference#files) |\n| OSDaR23 | Navtech 雷达 | 铁路专用目标检测 | [网站](https:\u002F\u002Fgithub.com\u002FDSD-DBS\u002Fraillabel) |\n\n### 惯性导航与定位\n| 数据集 | 雷达类型 | 任务 | 链接 |\n| ---- |----| ---- | ---- | \n| 牛津越野雷达数据集 | Navtech 旋转雷达 | 场所识别 |  [网站](oxford-robotics-institute.github.io\u002Foord-dataset) |\n| 牛津雷达机器人车 | Navtech 旋转雷达 | 惯性导航、（目标）检测 | [网站](https:\u002F\u002Foxford-robotics-institute.github.io\u002Fradar-robotcar-dataset\u002F)；[检测标注](https:\u002F\u002Fgithub.com\u002Fqiank10\u002FMVDNet) |\n|RADIATE| Navtech 旋转雷达 | 惯性导航、目标检测、跟踪 | [网站](http:\u002F\u002Fpro.hw.ac.uk\u002Fradiate\u002Fdoc\u002Fdataset\u002F)|\n| MulRan | Navtech 旋转雷达 | 场所识别 |[网站](https:\u002F\u002Fsites.google.com\u002Fview\u002Fmulran-pr\u002Fdataset)|\n| Boreas | Navtech 旋转雷达|   长期惯性导航、定位、目标检测 | [网站](https:\u002F\u002Fwww.boreas.utias.utoronto.ca\u002F#\u002F)|\n| EU 长期数据集 | Conti ARS 308 | 长期 SLAM | [网站](https:\u002F\u002Fepan-utbm.github.io\u002Futbm_robocar_dataset\u002F)|\n| ColoRadar | TI AWR2243 级联 + AWR1843 |惯性导航 |  [网站](https:\u002F\u002Farpg.github.io\u002Fcoloradar\u002F) |\n| USVInland | TI AWR1843 | 内陆水道 SLAM，水域分割| [网站](http:\u002F\u002Forca-tech.cn\u002Fdatasets\u002FUSVInland\u002FIntroduction) |\n| Endeavour 雷达数据集 | Conti ARS 430 x5 | 惯性导航 | [网站](https:\u002F\u002Fgloryhry.github.io\u002F2021\u002F06\u002F25\u002FEndeavour_Radar_Dataset.html)|\n| OdomBeyondVision |  TI AWR1843 | 惯性导航 | [网站](https:\u002F\u002Fgithub.com\u002FMAPS-Lab\u002FOdomBeyondVision) |\n\n\n### 手势\n| 数据集 | 雷达类型 | 数据类型 | 任务 | 链接 |\n| ---- |----| ---- | ---- | ---- | \n| DopNet | 24GHz | 频谱图 | 手势 | [网站](http:\u002F\u002Fdop-net.com\u002F)|\n| MCD-Gesture | 77GHz | RAD 张量 |手势 | [网站](https:\u002F\u002Fgithub.com\u002FDI-HGR\u002Fcross_domain_gesture_dataset)|\n| DeepSoli | 60GHz | RD 图 | 手势 | [网站](https:\u002F\u002Fgithub.com\u002Fsimonwsw\u002Fdeep-soli) | \n| Pantomime | TI IWR1443 | PC | 手势  | [数据集](https:\u002F\u002Fzenodo.org\u002Frecord\u002F4459969) |\n| MIMOGR | | RADT | 手势 | [网站](https:\u002F\u002Fgithub.com\u002FTkwer\u002FGesture-Recognition-Based-on-mmwave-MIMO-Radar) |\n\n### 人体活动与重建\n| 数据集 | 雷达类型 | 数据类型 | 任务 | 链接 |\n| ---- |----| ---- | ---- | ---- | \n| 人体活动的雷达特征  | 5.8 GHz | ADC | 人体活动 | [数据集](http:\u002F\u002Fresearchdata.gla.ac.uk\u002F848\u002F) |\n| Ci4R 人体活动数据集 | 77GHz & 24GHz & 10GHz |频谱图 | 人体活动 | [网站](https:\u002F\u002Fgithub.com\u002Fci4r\u002FCI4R-Activity-Recognition-datasets\u002F) |\n| RadHAR | 77GHz | 点云 | 人体活动  | [网站](https:\u002F\u002Fgithub.com\u002Fnesl\u002FRadHAR) |\n| mRI | 77GHz| 点云、RGBD 相机、IMU | 人体姿态估计  | [网站](https:\u002F\u002Fsizhean.github.io\u002Fmri)|\n| mmBody  | Arbe Phoenix 4D 雷达 | 点云、RGBD  | 3D 身体重建 | [网站](https:\u002F\u002Fchen3110.github.io\u002Fmmbody\u002Findex.html) ||\n| HuPR | 2 TI 1843 | RAD | 姿态 | [Github](https:\u002F\u002Fgithub.com\u002Frobert80203\u002FHuPR-A-Benchmark-for-Human-Pose-Estimation-Using-Millimeter-Wave-Radar) |\n\n\n\n### 生命体征\n| 数据集 | 雷达类型 | 数据类型 | 任务 | 链接 |\n| ---- |----| ---- | ---- | ---- | \n| 儿童生命体征 | 60GHz| ADC  | 心跳、呼吸 | [数据集](https:\u002F\u002Ffigshare.com\u002Fs\u002F936cf9f0dd25296495d3) |\n| GUARDIAN 生命体征 | 24GHz | IQ | 心跳、呼吸 | [数据集 1](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FA_dataset_of_clinically_recorded_radar_vital_signs_with_synchronised_reference_sensor_signals\u002F12186516?file=22515785) [数据集 2](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FA_dataset_of_radar-recorded_heart_sounds_and_vital_signs_including_synchronised_reference_sensor_signals\u002F9691544?backTo=\u002Fcollections\u002FGUARDIAN_Vital_Sign_Data\u002F4633958)|\n| 多人定位与生命体征估计雷达数据集 |  | |  | [数据集](https:\u002F\u002Fieee-dataport.org\u002Fopen-access\u002Fmulti-person-localization-and-vital-sign-estimation-radar-dataset) |\n\n---\n\n## 雷达工具箱\n### 仿真\nRadarSimPy: [代码](https:\u002F\u002Fgithub.com\u002Frookiepeng\u002Fradarsimpy);\u003Cbr>\nVirtual Radar: [代码](https:\u002F\u002Fgithub.com\u002Fchstetco\u002Fvirtualradar);\u003Cbr>\nMaxRay: [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.01751);\u003Cbr>\nRadaRays: [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10845807), [代码](https:\u002F\u002Fgithub.com\u002Fuos\u002Fradarays), [视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fLH8JPYk67o)\n\n### TI 信号处理 SDK:\nRaDICaL 的工具箱: [SDK](https:\u002F\u002Fgithub.com\u002Fmoodoki\u002Fradical_sdk); \u003Cbr>PyRapid: [SDK](http:\u002F\u002Fradar.alizadeh.ca);\u003Cbr>OpenRadar : [SDK](https:\u002F\u002Fgithub.com\u002Fpresenseradar\u002Fopenradar);\u003Cbr>Pymmw: [SDK](https:\u002F\u002Fgithub.com\u002Fm6c7l\u002Fpymmw);\u003Cbr>Open radar initiative: [SDK](https:\u002F\u002Fgithub.com\u002Fopenradarinitiative);\u003Cbr>RADIal 的 Emptyband-DDM 脚本: [代码](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FRADIal\u002Ftree\u002Fmain\u002FSignalProcessing) \n\n### 官方 SDK:\nNXP 高级雷达 SDK: [链接](https:\u002F\u002Fwww.nxp.com\u002Fdesign\u002Fautomotive-software-and-tools\u002Fpremium-radar-sdk-advanced-radar-processing:PREMIUM-RADAR-SDK);\u003Cbr>TI mmWAVE Studio: [链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FMMWAVE-STUDIO);\u003Cbr>TI 工具箱: [链接](https:\u002F\u002Fdev.ti.com\u002Ftirex\u002Fexplore\u002Fnode?node=AHJY4qNCowO17wH-P2ICKQ);\u003Cbr>Matlab 雷达工具箱: [链接](https:\u002F\u002Fuk.mathworks.com\u002Fproducts\u002Fradar.html)\n\n### 数据采集：\nTI 雷达和摄像头的 Python 实现：[代码](https:\u002F\u002Fgithub.com\u002Fyizhou-wang\u002Fcr-data-collector)；\u003Cbr>\nAinstein 雷达 ROS 节点：[ROS 节点](https:\u002F\u002Fgithub.com\u002FAinsteinAI\u002Fainstein_radar)；\u003Cbr>大陆 ARS 408 ROS 节点：[ROS 节点](https:\u002F\u002Fgitlab.com\u002FApexAI\u002Fautowareclass2020\u002F-\u002Ftree\u002Fmaster\u002Fcode\u002Fsrc\u002F09_Perception_Radar\u002FRadar-Hands-On-WS)；\u003Cbr>TI 毫米波 ROS 驱动程序：[指南](https:\u002F\u002Fdev.ti.com\u002Ftirex\u002Fexplore\u002Fnode?node=ADINBw2NDaxb6JeW7V-lMQ__VLyFKFf__LATEST&search=ROS)；\u003Cbr>RaDICaL 的 TI ROS 节点：[ROS 节点](https:\u002F\u002Fgithub.com\u002Fmoodoki\u002Fiwr_raw_rosnode)；\u003Cbr>雅典大学的 TI ROS 软件包：[ROS 节点](https:\u002F\u002Fgithub.com\u002Fradar-lab\u002Fti_mmwave_rospkg)\n\n---\n\n## 研讨会与工作坊\n\n* 2021 ICRA 全天候自主驾驶中的雷达感知 [[官网]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fradar-robotics\u002Fhome)\n* 2021 ICASSP 自动驾驶车辆中毫米波雷达传感的最新进展 [[官网]](https:\u002F\u002Fwww.2021.ieeeicassp.org\u002FPapers\u002FViewSession_MS.asp?Sessionid=1280)\n* 弗劳恩霍夫 FHR 主办的“Radar in Action”系列讲座 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fhashtag\u002Fradarinaction)\n* IEEE AESS 虚拟杰出讲师网络研讨会系列 [[官网]](https:\u002F\u002Fieee-aess.org\u002Factivities\u002Feducational-activities\u002Fdistinguished-lecturers)\n* 《雷达学报》网络研讨会系列（中文） [[视频]](https:\u002F\u002Fspace.bilibili.com\u002F1288394672)\n\n* 马库斯·加迪尔：汽车雷达——最新技术概览 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=P-C6_4ceY64&t=2416s)\n* 马库斯·加迪尔：汽车雷达——从信号处理角度看当前技术和未来系统 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IxoPYhXY30k&t=11s)[[幻灯片]](https:\u002F\u002Fcloud.gardill.net\u002Fs\u002FtjoSLSB7fXWTEBb)\n* 弗朗切斯科·菲奥拉内利：雷达虽旧犹新——当前研究挑战及雷达微多普勒特征方面的活动 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ysL6rk-4L9o&list=PLa5-fgjZm9MtJBtb6M3YplIDSdgr1m94n&index=3)\n* 特斯拉的安德烈·卡帕西：[[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=g6bOwQdCJrc)\n* 奥莱·舒曼：用于自动驾驶的雷达感知——数据与方法 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UQL7_Zy2Kjg&t=73s)\n* Arbe 公司的萨尼·罗嫩：利用 AI 层将高分辨率雷达转化为自动驾驶应用的洞察 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=TtJW-c02YH8)\n* 斯特凡·哈格：为机器学习与传感器融合改进系统共同开发自动标注功能 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ANbmXg2TxlE)\n* 阿瑟·欧阿克宁：深度学习与场景理解在自动驾驶车辆中的应用 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sOOvnTCnhPg)\n* 保罗·纽曼：通往任何地方的自主之路 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MzYQMRG9HW0)\n* 海梅·连：Soli：用于无接触交互的毫米波雷达 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JFr8Whnx630)\n* 加速软件定义 4D 成像雷达的端到端开发 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QPoj1zM2vCs)\n* 雷达成像——理论入门 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ej2smyTZLHE)\n* NXP：雷达专家探讨汽车雷达的发展历程 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RfJiiSlesyE&t=347s)\n* 需要成功设计毫米波汽车雷达天线吗？[[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0lK8qJSWY_c)\n* 使用 Ancortek 软件定义雷达进行 SAR 成像网络研讨会 [[视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BMSVvQJYCIs)\n* TI：管理 FMCW 雷达系统中的干扰 [[视频]](https:\u002F\u002Ftraining.ti.com\u002Fmanaging-interference-fmcw-radar-systems)\n\n\n---\n\n## 综述论文\n\n雷达硬件：\n* [77 GHz频段车载雷达传感器的毫米波技术](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6127923)\n* [自动驾驶车辆与智能交通系统中的芯片级\u002F封装内雷达](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8830483)\n* [毫米波车载雷达传感器的天线设计](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6165323)\n* [具有1728个虚拟通道的79 GHz高分辨率4D成像MIMO雷达系统性能](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9866614)\n\n\n雷达信号处理：\n* 通用：[自动驾驶汽车中雷达的兴起：信号处理解决方案及未来研究方向](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828025\u002F)\n* 信号处理：[车载雷达信号处理：研究方向与实际挑战](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9369027)\n* 信号处理：[车载雷达——信号处理技术综述](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7870764)\n* 信号处理：[车载雷达技术进展：一种计算高效的高分辨率频率估计框架](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7870737)\n* MIMO：[用于高级驾驶辅助系统和自动驾驶的MIMO雷达：优势与挑战](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9127853)\n* DOA：[毫米波雷达的校准与到达角估计：实用入门](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9099537)\n* 数字雷达：[高性能车载雷达：信号处理算法与调制方案综述](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828004)\n* 微多普勒：[雷达中的微多普勒效应：现象、模型及仿真研究](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1603402)\n* 动态范围：[现代数字阵列雷达的动态范围考量](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9114607)\n* 相位噪声：[迈向安全自动驾驶之路：雷达传感器中相位噪声监测以满足功能安全要求](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8827996)\n* 相位噪声：[FMCW雷达系统中相位噪声与系统性相位失真的详细分析与建模](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9875949)\n* 干扰：[自动驾驶中雷达干扰抑制：探索主动策略](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9127843)\n* 干扰：[车载雷达系统中的干扰：特性、抑制技术以及当前和未来的研究](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828037)\n\n\n\n车载雷达应用：\n* 检测与融合用于自动驾驶：[迈向自动驾驶的深度雷达感知：数据集、方法与挑战](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F22\u002F11\u002F4208)\n* 信号处理：[车载雷达信号处理：研究方向与实际挑战](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9369027)\n* 干扰：[利用深度学习进行干扰抑制：当前方法与开放挑战](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9802083)\n* 语义理解：[自动驾驶中的雷达——从单纯检测到语义环境理解的范式转变](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-658-23751-6_1)\n* 雷达与激光雷达：[车载应用中雷达与激光雷达技术的比较分析](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9760734)\n\n其他雷达应用：\n* 人体活动识别：[基于传感器的人体活动识别深度学习：概述、挑战与机遇](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3447744)\n* 手势：[利用雷达进行运动感知：手势交互及其他](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8755821)\n* 生命体征：[非接触式雷达传感器：多主体生命体征监测的最新进展](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9785580)\n* 无人机：[自主无人飞行器的雷达感知：综述](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3522784.3522787)\n\n通用目标检测：\n* [面向自动驾驶的图像三维目标检测：综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.02980)\n* [深度学习在三维点云中的应用：综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.12033)\n* [计算机视觉中的注意力机制：综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.07624)\n* [自动驾驶中概率性目标检测的回顾与比较研究](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.10671)\n\n\n\n传感器融合：\n\n* 学习：[自动驾驶中的多模态三维目标检测：综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12735)\n* 学习：[用于自动驾驶的深度多模态目标检测与语义分割：数据集、方法与挑战](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9000872)\n* 传统：[多传感器数据融合：现状综述](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1566253511000558)\n* 信息：[多源交互中目标效应的信息分解：对过去、当前及未来工作的展望](https:\u002F\u002Fwww.mdpi.com\u002F1099-4300\u002F20\u002F4\u002F307)\n* 不确定性：[机器学习中的偶然不确定性和认知不确定性：概念与方法简介](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-021-05946-3)\n* 同余预测：[自动驾驶中概率性目标检测的回顾与比较研究](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.10671)\n\n\n\n\n---\n\n## 推荐书籍与教程\n### 雷达教材\n* 《雷达信号处理基础》作者：Mark A. Richard\n* 《使用Matlab进行雷达系统分析与设计》作者：Bassem R. Mahafza \n### 在线课程\n* [雷达：雷达系统导论](https:\u002F\u002Fwww.ll.mit.edu\u002Foutreach\u002Fradar-introduction-radar-systems-online-course)\n* [构建雷达](https:\u002F\u002Fllx.mit.edu\u002Fcourses\u002Fcourse-v1:MITLL+MITLLx01+Q2_2019\u002Fabout)\n* [雷达系统工程](http:\u002F\u002Fradar-course.org\u002F)\n* [自适应天线与相控阵](https:\u002F\u002Fwww.ll.mit.edu\u002Foutreach\u002Fadaptive-antennas-and-phased-arrays-online-course)\n\n### 信号处理\n* [毫米波感知入门：FMCW雷达](https:\u002F\u002Ftraining.ti.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fdocs\u002FmmwaveSensing-FMCW-offlineviewing_0.pdf)\n* [毫米波传感器的基础知识](https:\u002F\u002Fwww.ti.com\u002Flit\u002Fwp\u002Fspyy005a\u002Fspyy005a.pdf?ts=1619205965675)\n* [TDM MIMO FMCW毫米波雷达传感器的信号处理](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9658500)\n* [散射中心到点云：面向非雷达工程师的毫米波雷达综述](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9908570)\n### 波形比较\n* [MIMO雷达波形的分析与比较](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7060251)\n\n### 正交信号\n* [正交信号：复杂但不难懂](https:\u002F\u002Fdspguru.com\u002Fdsp\u002Ftutorials\u002Fquadrature-signals\u002F) \n* [在FMCW雷达系统中使用复基带架构](https:\u002F\u002Fwww.ti.com\u002Flit\u002Fwp\u002Fspyy007\u002Fspyy007.pdf)\n### MIMO\n* [TI MIMO雷达](https:\u002F\u002Fwww.ti.com\u002Flit\u002Fan\u002Fswra554a\u002Fswra554a.pdf)\n* [TI EmptyBand DDM（中文）]() [(英文)]()\n\n---\n\n## 雷达特征\n### 点云\n* 2022年——面向扩展目标的汽车雷达探测随机建模的数据驱动方法 __`GeMic`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9783497)\n* 2020年——用于汽车应用的大孔径雷达性能评估 __`RadarConf`__; __`0.1Deg`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9266609)\n* 2018年——各类目标的雷达与激光雷达目标特征及自动驾驶应用中扩展目标跟踪方法的评估 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8455395)\n* 2017年——用于轮廓和特征估计的车辆雷达反射特性 __`FUSION`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8126352)\n\n### RCS\n* 2021年——基于机器学习的自动驾驶毫米波雷达目标分类 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9319548)\n* 2021年——基于大规模标注数据集的先进汽车雷达性能评估及其相应建模方法 [论文](https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Ffull\u002F10.1080\u002F15472450.2021.1959328)\n* 2021年——开放雷达倡议：用于微多普勒识别算法基准测试的大规模数据集 __`RadarConf`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455239)\n* 2018年——小型无人机或无人驾驶飞行器的雷达分类与RCS表征技术综述 [论文](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1049\u002Fiet-rsn.2018.0020)\n\n### 相位\n* 2019年——基于相位的神经网络目标分类在汽车雷达系统中的应用 __`RadarConf`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8835725)\n\n### 运动\n* 2021年——开放雷达倡议：用于微多普勒识别算法基准测试的大规模数据集 __`RadarConf`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455239)\n* 2020年——基于旋转散射体特征的道路安全新型雷达微多普勒标签 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9311635)\n* 2019年——利用雷达进行运动感知：手势交互及其他 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8755821)\n\n### 极化\n* 2019年——乘用车的极化特征 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8890117)\n* 2018年——79 GHz极化雷达传感器在自动驾驶中的性能分析 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8249142)\n* 2018年——基于79 GHz极化雷达数据的自动驾驶功能 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8546632)\n\n---\n\n## 校准\n### 雷达\n* 2022年——采用存在批量生产误差的角反射器进行雷达校准 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784534)\n* 2022年——一种用于路边毫米波雷达校准与验证的新方法 [论文](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1049\u002Fitr2.12151)\n* 2021年——利用同时定位与地图构建技术在运行模式下自动校准汽车雷达 __`TVT`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9353252)\n* 2020年——基于运动的自动驾驶应用中4D成像雷达在线校准 __`GeMic`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9080233)\n* 2018年——利用高精度数字地图实现自动驾驶的多雷达自校准方法 __`ITSC`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8569272\u002F)\n\n### 雷达-相机\n* 2021年——用于3D雷达到相机外参校准的连续时间方法 __`ICRA`__; __`运动`__ ; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9561938)\n* 2021年——基于高斯过程移动目标跟踪的时空多传感器校准 __`ToR`__; __`轨迹`__ ; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9387269)\n* 2019年——用于智能交通系统的雷达与相机无目标旋转自动校准 __`ITSC`__; __`NN`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8917135)\n* 2015年——用于户外3D重建的雷达与视觉传感器校准 __`ICRA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7139473)\n* 2004年——利用毫米波雷达进行障碍物检测并在图像序列中可视化 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1334537\u002F)\n\n### 雷达-激光雷达\n* 2020年——汽车雷达与3D激光雷达的外参及时间同步校准 __`IROS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9341715)\n* 2020年——多个3D激光雷达和雷达的自动无目标外参校准 __`IROS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9340866\u002F)\n* 2017年——3D激光雷达与雷达的外参6自由度校准 __`RCS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8098688)\n\n### 雷达-激光雷达-相机\n* 基于静态视点序列对多传感器系统进行的持续无目标外参校准 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03785)\n* 2022年——OpenCalib：用于自动驾驶的多传感器校准工具箱 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14087); [代码](https:\u002F\u002Fgithub.com\u002FPJLab-ADG\u002FSensorsCalibration)\n* 2021年——用于雷达、相机和激光雷达的联合外参校准工具 __`TIV`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9380784); [代码](https:\u002F\u002Fgithub.com\u002Ftudelft-iv\u002Fmulti_sensor_calibration)\n* 2021年——基于移动目标跟踪的在线多传感器校准 [论文](https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Ffull\u002F10.1080\u002F01691864.2020.1819874)\n* 2019年——结合雷达截面估算评估的雷达–激光雷达–相机系统的外参6自由度校准 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0921889018301994)\n\n---\n\n## 标注\n* 2021年——基于3D雷达立方体的半监督学习时空一致性 __`IV`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9575247)\n* 2021年——多传感器数据中弱势道路使用者的自动标注 __`ITSC`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564692)\n* 2021年——重新思考雷达的作用：基于坐标对齐的相机-雷达数据集与系统化标注工具 __`CVPRW`__; __`CRUW`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021W\u002FWAD\u002Fhtml\u002FWang_Rethinking_of_Radars_Role_A_Camera-Radar_Dataset_and_Systematic_Annotator_CVPRW_2021_paper.html)\n* 2021年——RADDet：面向动态道路使用者的基于距离-方位-多普勒的雷达目标检测 __`CRV`__; __`RADDet`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9469418)\n* 2020年——CARRADA数据集：带有距离-角度-多普勒标注的摄像头与车载雷达 __`ICPR`__; __`CARRADA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9413181)\n* 2020年——雷达伪影标注框架（RALF）：用于数据集中合理雷达检测的方法 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.01993)\n* 2020年——高效标注车载雷达：语义雷达标注框架（SeRaLF） [论文](https:\u002F\u002Fml4ad.github.io\u002Ffiles\u002Fpapers2020\u002FAnnotating%20Automotive%20Radar%20efficiently:%20Semantic%20Radar%20Labeling%20Framework%20(SeRaLF).pdf)\n* 2020年——RSS-Net：基于FMCW雷达的弱监督多类别语义分割 __`IV`__; __`Oxford`__; __`PoseChain`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9304674)\n* 2019年——利用GNSS在车载雷达数据中自动化估计弱势道路使用者的真实标签 __`ICMIM`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8726801)\n\n---\n\n\n## 数据增强\n* 2022年——代尔夫特视图数据集中基于3+1D雷达的多类别道路使用者检测 __`RAL`__; __`PC`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9699098)\n* 2021年——深度学习方法在雷达光谱上进行目标分类的不确定性研究 __`RadarConf`__; __`RA`__; __`Corruption`__ ; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9455269)\n* 2021年——用于基于雷达的手势识别的时间与多普勒频域数据增强 __`EuRad`__; __`Spectrogram`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784553)\n* 2020年——RAMP-CNN：一种用于增强车载雷达目标识别的新神经网络 __`RA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9249018)\n* 2020年——RADIO：针对小规模数据集的参数化生成式雷达数据增强 __`RA`__; [论文](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F10\u002F11\u002F3861)\n* 2016年——结合数据增强的卷积神经网络用于SAR目标识别 __`GRSL`__; __`SAR`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7393462)\n\n\n---\n\n## 模拟器\n* 2024年——RadSimReal：通过仿真弥合雷达目标检测中合成与真实数据之间的鸿沟 __`CVPR`__;\n* 2021年——MaxRay：基于光线追踪的集成感知与通信框架 __`OpenSoucre`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9743510)\n* 2021年——虚拟雷达：用于感知驱动型机器人技术的实时毫米波雷达传感器仿真 __`RAL`__; __`OpenSoucre`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9387149); [代码](https:\u002F\u002Fgithub.com\u002Fchstetco\u002Fvirtualradar)\n* 2020年——用于交互式数字孪生的可扩展且物理化的雷达传感器仿真[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9205224)\n\n### 伪影\n* 2020年——复杂车载多用户交通场景下干扰分析的模拟器设计 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9266318)\n* 2019年——用于自动驾驶虚拟测试的雷达传感器伪影建模与仿真 [论文](https:\u002F\u002Fmediatum.ub.tum.de\u002F1535151)\n\n### 评估\n* 2021年——深度评估指标：学习评估用于自动驾驶虚拟测试的仿真雷达点云 __`RadarConf`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455235)\n* 2021年——一种多层方法，用于衡量自动驾驶雷达感知的仿真到现实差距 __`IV`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564521)\n* 2018年——揭示自动驾驶虚拟验证中雷达传感器建模挑战的测量结果 __`ITSC`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8569423)\n\n\n---\n\n## 生成模型\n* 2021年——来去之间：学习为实际应用模拟雷达数据 __`ICRA`__; __`Simulation_to_RA`__; __`Categorical_VAE`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.14389)\n* 2020年——L2R GAN：激光雷达到雷达的转换 __`ACCV`__; __`Lidar_OGM_to_RD`__; __`Oxford`__ ;__`cGAN`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2020\u002Fpapers\u002FWang_L2R_GAN_LiDAR-to-Radar_Translation_ACCV_2020_paper.pdf)\n* 2020年——GenRadar：基于雷达频率的自监督概率性相机合成 __`Journal`__; __`RD_to_Image`__; __`Categorical_VAE`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.08948)\n* 2019年——使用生成对抗网络实现车载雷达与摄像头融合 __`Journal`__; __`Radar_OGM_to_Image`__; __`cGAN`__; [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1077314219300530)\n* 2017年——深度随机雷达模型 __`IV`__; __`Scene_to_RA`__; __`VAE`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7995697)\n\n### 多普勒\n* 2021年——IMU2Doppler：利用IMU数据进行基于多普勒的活动识别的跨模态领域适应 __`IMWUT`__; __`IMU`__; [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3494994)\n* 2021年——Vid2Doppler：从视频中合成多普勒雷达数据以训练保护隐私的活动识别 __`CHI`__; __`Video`__; [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3411764.3445138)\n\n---\n\n## 测试\n* 2021年——智能车辆中的毫米波雷达在环测试 __`TITS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9507048)\n* 2020年——借助目标模拟器测试先进车载雷达传感器 [论文](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F9\u002F2714)\n* 2017年——用于毫米波车载雷达防碰撞测试的替代自行车设计 __`TITS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7829378)\n\n\n---\n\n## MIMO校准\n* 2021年——利用同时定位与地图构建在运行模式下自动校准车载雷达 __`TVT`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9353252)\n* 2021年——MIMO雷达系统的精确校准实用方案 __`EuRAD`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784495)\n\n---\n\n## 检测器\n* 2022年—一种用于机器人环境感知的新型雷达点云生成方法 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9823311)\n* 2021年—基于深度神经网络的峰值序列分类CFAR检测算法，适用于高分辨率FMCW雷达 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9547416)\n* 2020年—从雷达图像中估计目标表面 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9054622)\n* 2020年—深度时序检测：一种用于多驻留目标检测的机器学习方法 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9114828)\n* 2019年—DL-CFAR：一种基于深度学习的新型CFAR目标检测方法 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8891420)\n* 2019年—在稀疏三维点云数据中实现恒定虚警率的特征检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8924890)\n\n## 超分辨率\n* 2024年—DART：用于雷达新视角合成的隐式多普勒层析成像 __`CVPR`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.03896)；[代码](https:\u002F\u002Fgithub.com\u002FWiseLabCMU\u002Fdart)\n* 2023年—面向FMCW毫米波感知系统的数据驱动空间超分辨率 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10287476)\n* 2023年—利用经增强虚拟数据训练的深度神经网络，实现稀疏阵列下的雷达超分辨率成像 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.09839)\n* 2023年—自动驾驶中FMCW雷达的方位角超分辨率 __`CVPR`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLi_Azimuth_Super-Resolution_for_FMCW_Radar_in_Autonomous_Driving_CVPR_2023_paper.html)\n* 2023年—自监督学习用于提升车载MIMO雷达的角度分辨率 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10106481)\n* 2022年—从机器学习视角看车载雷达到达角估计 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9674901)\n* 2020年—使用带领域适应的参数化变分自编码器，从原始ADC数据重建雷达图像 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9412858)\n\n## 聚类\n\n* 2021年—面向车载4D雷达聚类的监督降噪 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9659953)\n* 2019年—用于车载雷达数据的多阶段聚类框架 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8916873)\n* 2019年—针对毫米波雷达点云数据的鲁棒且自适应的基于椭圆密度的空间聚类与标注 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9048869)\n* 2018年—面向雷达应用的监督聚类：迈向雷达实例分割 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8443534)\n* 2016年—用于高分辨率雷达车辆轮廓估计的自适应聚类 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7533930)\n* 2012年—基于网格的DBSCAN，用于雷达数据中扩展目标的聚类 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6232167)\n\n\n## 去噪\n* 将深度卷积自编码器应用于FMCW雷达距离-多普勒图的降噪\n* 从自然噪声中学习以去噪微多普勒谱图\n\n\n---\n\n## TI参考设计\n* TIDEP-01027 高端角雷达参考设计 __`AWR2944`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01027)\n* TIDEP-01025 毫米波诊断与监测参考设计 [链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01025)\n* TIDEP-01024 使用76至81GHz片上天线（AoP）毫米波传感器的障碍物检测参考设计 __`AWR1843AOP`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01024)\n* TIDEP-01023 使用60GHz片上天线毫米波传感器的儿童存在及乘员检测参考设计 __`AWR6843AOP`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01023)\n* TIDEP-01021 用于角雷达参考设计的波束转向 __`AWR1843BOOST`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01021)\n* TIDEP-01018 使用TI毫米波传感器的自动门参考设计 __`IWR6843ISK`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01018)\n* TIDEP-01013 使用毫米波传感器和Sitara™处理器的手势控制人机界面参考设计 __` IWR6843ISK`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01013)\n* TIDEP-01012 使用级联毫米波传感器的成像雷达参考设计 __`AWR2243`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01012)\n* TIDEP-01011 使用77GHz毫米波传感器的自动泊车系统参考设计 __`AWR1843`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01011)\n* TIDEP-01010 使用集成片上天线毫米波传感器的区域扫描参考设计 __`IWR6843`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01010)\n* TIDEP-0104 使用77GHz毫米波传感器的障碍物检测参考设计 __`AWR1642`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0104)\n* TIDEP-01006 使用ROS在Sitara™ MPU及片上天线毫米波传感器上的自主机器人参考设计 __`IWR6843`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01006)\n* TIDEP-01003 使用毫米波传感器的区域占用检测参考设计 __` IWR1443BOOST`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01003) \n* TIDEP-01001 车辆乘员检测参考设计 __`AWR6843`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01001)\n* TIDEP-01000 使用毫米波雷达传感器的人数统计与跟踪参考设计 __`IWR6843`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-01000)\n* TIDEP-0094 80米范围物体检测参考设计，配备集成单芯片毫米波传感器 __`IWR1642`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0094)\n* TIDEP-0092 短程雷达（SRR）参考设计 __`IWR1642`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0092)\n* TIDEP-0091 77GHz级别发射机功率优化参考设计 __`IWR1443`__；[链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0091)\n* TIDEP-0090 使用毫米波雷达传感器的交通监控、物体检测与跟踪参考设计 [链接](https:\u002F\u002Fwww.ti.com\u002Ftool\u002FTIDEP-0090)\n\n\n---\n\n\n\n### 聚类分类\n* 2015年—[让贝莎看得更清楚](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7161279)\n* 2017年—[随机森林与长短期记忆网络在雷达分类任务中的性能比较](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8126350)\n* 2018年—[基于雷达的特征设计与多分类，用于道路使用者识别](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8500607)\n* 2019年—[基于雷达的道路使用者分类与新颖性检测，采用循环神经网络集成](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8813773)\n* 2020年—[现成传感器 vs. 实验性雷达——汽车雷达分类需要多高的分辨率？](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9190338)\n* 2021年—[利用雷达反射进行汽车目标分类的深度学习](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9455334) \n\n\n\n---\n\n## 目标检测\n* 2024年——自监督学习驱动的自主雷达系统自举 __`CVPR`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.04519)\n* 2024年——RadarDistill：通过从激光雷达特征中蒸馏知识提升基于雷达的目标检测性能 __`CVPR`__;\n* 2024年——SIRA：面向雷达感知的可扩展帧间关系与关联方法 __`CVPR`__;\n* 2023年——SMURF：用于4D成像雷达3D目标检测的空间多表示融合 __`VOD`__; __`TJ4D`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.10784)\n* 2023年——PeakConv：用于雷达语义分割的峰值感受野学习 __`CVPR`__;  [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.html)\n* 2023年——增强型K-Radar：通过优化密度降低提升基于4D雷达张量的目标检测性能与易用性 __`KRadar`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06342)\n* 基于目标检测网络的车载雷达子采样：利用先验信号信息 __`Oxford`__; __`RADIATE`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10450)\n* 2022年——用于原始雷达帧在线目标检测的循环CNN __`CARRADA`__; __`ROD`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.11172)\n* 2022年——Radatron：基于多分辨率级联MIMO雷达的精确目标检测 __`ECCV`__; [论文](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-19842-7_10)\n* 2022年——用于噪声雷达数据语义分割的高斯雷达Transformer __`Segmentation`__; __`RadarScenes`__; __`RAL`__; [论文](https:\u002F\u002Fwww.ipb.uni-bonn.de\u002Fwp-content\u002Fpapercite-data\u002Fpdf\u002Fzeller2022ral.pdf)\n* 2022年——面向多帧4D车载毫米波雷达点云的3D目标检测 __`3DDetection`__; __`TJ4D`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9944629)\n* 2022年——mmWave-YOLO：基于毫米波成像雷达的ADAS应用实时多类目标识别系统 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9777730)\n* 2022年——NVRadarNet：面向自动驾驶的实时雷达障碍物与自由空间检测 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.14499)\n* 2022年——ERASE-Net：用于车载雷达信号的有效分割网络 __`Segmentation`__; __`CARRADA`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.12940)\n* 2022年——用于多任务学习的原始高清雷达 __`CVPR`__; __`RADIAL`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FRebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html)\n* 2022年——利用雷达感知中的时间关系实现自动驾驶 __`CVPR`__; __`Oxford`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html)\n* 2022年——基于车载雷达检测点的深度实例分割 __`TIV`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9762032)\n* 2022年——结合混合目标检测网络提升车载雷达的方位估计与检测精度\n* 2022年——HARadNet：采用层次化注意力机制和多任务学习的无锚框雷达点云目标检测\n* Radar-PointGNN：基于图的方法用于非结构化雷达点云数据的目标识别\n* 2021年——用于3D目标检测的雷达体素融合 [论文](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F12\u002F5598); [代码](https:\u002F\u002Fgithub.com\u002FTUMFTM\u002FRadarVoxelFusionNet)\n* 2021年——基于深度学习的环境感知中不确定性量化 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.03018.pdf)\n* 2021年——用于雷达数据3D目标检测的图卷积网络 __`ICCVW`__; __`3DDetection`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021W\u002FAVVision\u002Fhtml\u002FMeyer_Graph_Convolutional_Networks_for_3D_Object_Detection_on_Radar_Data_ICCVW_2021_paper.html)\n* 2021年——使用弱监督学习进行高分辨率雷达道路分割 __`Segmentation`__; [论文](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-020-00288-6)[代码](https:\u002F\u002Fgithub.com\u002Fitaiorr\u002Fradar_road_seg)\n* 2021年——多视角雷达语义分割 __`ICCVW`__; __`CARRADA`__; __`Segmentation`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fhtml\u002FOuaknine_Multi-View_Radar_Semantic_Segmentation_ICCV_2021_paper.html); [代码](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FMVRSS)\n* 2021年——RPFA-Net：用于3D目标检测的4D雷达柱状特征注意力网络 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564754\u002F)\n* 2021年——利用Pointnets实现雷达点云中的行人检测 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459066.3459067)\n* 2021年——基于点云卷积的雷达检测语义分割 [论文](https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F1742-6596\u002F1924\u002F1\u002F012003\u002Fpdf)\n* 2021年——基于神经网络的高效雷达点云语义分割系统 [论文](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11063-021-10544-4)\n* 2020年——利用雷达特征改进神经网络对点云的分割 [论文](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-48791-1_8)\n* 2019年——毫米波车载雷达试验平台实验 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9048939)\n* 2019年——利用距离-方位-多普勒张量上的深度学习进行车载雷达车辆检测 [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCVW_2019\u002Fhtml\u002FCVRSUAD\u002FMajor_Vehicle_Detection_With_Automotive_Radar_Using_Deep_Learning_on_Range-Azimuth-Doppler_ICCVW_2019_paper.html)\n* 2020年——基于CNN的3D雷达立方体道路使用者检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8962258)\n* 2020年——RAMP-CNN：一种用于增强车载雷达目标识别的新式神经网络 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9249018)\n* 2019年——利用PointNets在雷达数据中进行2D车辆检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917000)\n* 2020年——利用深度学习对车载雷达数据进行检测与跟踪 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9190261)\n* 2020年——使用多雷达进行点阵式精确3D边界框估计 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3384419.3430783)\n* 2020年——利用深度神经网络进行基于雷达的2D车辆检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9294546)\n* 2021年——用于3D目标检测的雷达体素融合 [论文](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F12\u002F5598)\n* 2021年——用于雷达点云分割的核点卷积LSTM网络 [论文](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F6\u002F2599)\n* 2019年——深度雷达探测器 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8835792)\n* 2019年——利用毫米波雷达追踪和识别人员 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8804831)\n* 2020年——针对嵌入式应用优化的高分辨率车载雷达DBSCAN改进版 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9253774)\n* 2020年——在多模态交通监控中使用GMM进行毫米波雷达点云分割 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9114662)\n* 2020年——利用毫米波雷达实现穿雾高分辨率成像 [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FGuan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html)\n* 2020年——以雷达为中心的3D目标检测中的深度学习 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.00851)\n* 2021年——雷达Transformer：基于4D毫米波成像雷达的对象分类网络 [论文](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F21\u002F11\u002F3854)\n* 2021年——mmPose-NLP：一种基于自然语言处理的毫米波雷达精确骨骼姿态估计方法 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.10327)\n\n### CFAR 之前的数据\n#### 距离-方位图\n* 2019年—[基于深度学习的车载雷达频谱目标分类](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8835775)\n* 2020年—[车载高分辨率雷达图像中的图像分割与区域分类](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9288850)\n* 2020年—[基于 YOLO 的车载 FMCW 雷达系统中目标检测与分类同时进行](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F10\u002F2897)\n* 2020年—[基于深度神经网络和迁移学习的 300 GHz 雷达目标识别](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.03157)\n* 2020年—[2020年—车载雷达中的概率导向目标检测](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2020\u002Fhtml\u002Fw6\u002FDong_Probabilistic_Oriented_Object_Detection_in_Automotive_Radar_CVPRW_2020_paper.html)\n* 2021年—[恶劣天气条件下通过 2D-MIMO FMCW 车载雷达进行感知](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.01639)\n* 2021年—[基于深度学习的车载雷达二值距离-角度图上的去中心化帧间轨迹预测]()\n\n#### 距离-多普勒图\n* 2018年—[使用 77 GHz FMCW 雷达传感器和卷积神经网络进行单帧易受伤害道路使用者分类](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8448126)\n* 2018年—[利用卷积循环神经网络在车载雷达系统中进行运动目标分类](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8553185)\n* 2019年—[基于深度神经网络的雷达目标检测研究](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8629967)\n* 2020年—[利用全卷积网络通过 FMCW 雷达进行目标检测与三维估计](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9054511)\n* 2021年—[利用线性调频雷达的距离-多普勒关系检测高速机动目标](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9347707)\n* 2021年—[DeepHybrid：用于目标分类的车载雷达频谱及反射信号上的深度学习](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564526)\n\n#### ROD2021 挑战赛论文\n* 2021年—[利用数据合并、增强与融合进行雷达目标检测](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463653)\n* 2021年—[基于挤压-激励网络并结合加权位置融合的雷达目标检测](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3460426.3463654)\n* 2021年—[面向雷达目标检测的场景感知学习网络](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463655)\n* 2021年—[DANet：用于雷达目标检测的维度分离网络](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463656)\n* 2021年—[Efficient-ROD：基于密集连接残差网络的高效雷达目标检测](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3460426.3463657)\n* 2021年—[ROD2021 挑战赛：面向自动驾驶应用的雷达目标检测挑战赛综述](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460426.3463658)\n\n---\n\n## 传感器融合\n* 2024-RCBEVDet：基于鸟瞰图的雷达-相机融合用于3D目标检测 __`CVPR`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15883)\n* 2024-面向各种天气条件下的鲁棒3D目标检测：LiDAR与4D雷达融合 __`CVPR`__；\n* 2024-CRKD：基于跨模态知识蒸馏的增强型相机-雷达目标检测 __`CVPR`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhao_CRKD_Enhanced_Camera-Radar_Object_Detection_with_Cross-modality_Knowledge_Distillation_CVPR_2024_paper.html)\n* 2024-LiRaFusion：用于3D目标检测的深度自适应LiDAR-雷达融合 __`ICRA`__；__`nuScenes`__ [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.11735)\n* 2023-CRN：用于准确、鲁棒、高效3D感知的相机雷达网络 __`nuScenes`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FKim_CRN_Camera_Radar_Net_for_Accurate_Robust_Efficient_3D_Perception_ICCV_2023_paper.html)\n* 2023-LXL：利用4D成像雷达和相机融合进行LiDAR排除式轻量级3D目标检测 __`VOD`__；__`CVPR`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.00724)\n* 2023-RaLiBEV：用于无锚框目标检测系统的雷达与LiDAR BEV融合学习 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.06108)\n* 2023-Bi-LRFusion：用于3D动态目标检测的双向LiDAR-雷达融合 __`CVPR`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FWang_Bi-LRFusion_Bi-Directional_LiDAR-Radar_Fusion_for_3D_Dynamic_Object_Detection_CVPR_2023_paper.html)\n* 2023-MVFusion：具有语义对齐的雷达和相机融合的多视角3D目标检测 __`nuScenes`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10511)\n* 2022-RADIANT：用于3D目标检测的雷达-图像关联网络 __`AAAI`__；__`nuScenes`__；[论文](http:\u002F\u002Fcvlab.cse.msu.edu\u002Fpdfs\u002FLong_Kumar_Morris_Liu_Castro_Chakravarty_AAAI2023.pdf)\n* 2022-利用雷达与立体相机的遮挡感知传感器融合检测突然冲出的行人；__`TIV`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9941368)\n* 2022-CramNet：基于光线约束交叉注意力的相机-雷达融合，用于鲁棒3D目标检测；__`ECCV`__；__`RADIATE`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.09267)\n* DeepFusion：一种鲁棒且模块化的3D目标检测器，适用于LiDAR、相机和雷达 __`nuScenes`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.12729)\n* 2022-CRAFT：基于时空上下文融合Transformer的相机-雷达3D目标检测 __`nuScenes`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.06535)\n* 2022-弥合雷达与相机特征视图差异，实现多模态融合3D目标检测 __`BEVFeature`__；__`nuScenes`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12079)\n* 2022-RadSegNet：一种可靠的雷达-相机融合方法 __`RADIATE`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.03849)\n* 2022-HRFuser：用于2D目标检测的多分辨率传感器融合架构 __`CrossAttention`__；__`nuScenes`__；__`DENSE`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.15157)；[代码](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.15157)\n* 2022-无需LiDAR的BEV感知简单基线 __`BEVFeature`__；__`nuScenes`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07959)\n* 2022-用于车辆检测中雷达-LiDAR融合的模态无关学习 __`CVPR`__；__`TeacherStudent`__；__`Oxford_Foggy`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html)\n* 2022-用于毫米波雷达和相机的鲁棒目标检测的全局-局部特征增强网络 __`ICASSP`__；__`ROI+Transformer`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9746764)\n* 2022-雷达引导的动态视觉注意力，用于资源高效的RGB目标检测 __`IJCNN`__；__`RadarROI`__；__`nuScenes`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.01772)\n* 2021-RVDet：用于目标检测的雷达与相机特征级融合 __`ITSC`__；__`BEVFeature`__；__`鱼眼相机`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564627)\n* 2021-通过自动驾驶中的表征学习实现雷达-相机融合；__`CVPRW`__；__`VisualSemantics`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021W\u002FMULA\u002Fhtml\u002FDong_Radar_Camera_Fusion_via_Representation_Learning_in_Autonomous_Driving_CVPRW_2021_paper.html)；[视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kBkdw4qFznU&t=5s)\n* 2021-通过相机与毫米波雷达融合实现水面小目标的鲁棒检测 __`ICCV`__；__`Attention`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fhtml\u002FCheng_Robust_Small_Object_Detection_on_the_Water_Surface_Through_Fusion_ICCV_2021_paper.html)\n* 2021-在雾天条件下利用互补的LiDAR和雷达信号实现鲁棒的多模态车辆检测 __`CVPR`__；__`Attention`__；__`Oxford_Foggy`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fhtml\u002FQian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html)；[代码](https:\u002F\u002Fgithub.com\u002Fqiank10\u002FMVDNet)\n* 2021-CFTrack：基于中心点的雷达与相机融合，用于3D多目标跟踪 __`IV`__；__`CenterFusion+Track`__；__`nuScenes`__；[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.05150)；[视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_vuO19L6L0Q)\n\n\n* 2019-[利用雷达和视觉进行远距离车辆检测](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8794312)\n* 2019-[RVNet：用于复杂环境下基于图像的障碍物检测的单目相机与雷达深度传感器融合](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-34879-3_27)\n* 2019-[用于高级驾驶辅助系统的车辆检测中雷达与相机的早期融合](https:\u002F\u002Fml4ad.github.io\u002Ffiles\u002Fpapers\u002FRadar%20and%20Camera%20Early%20Fusion%20for%20Vehicle%20Detection%20in%20Advanced%20Driver%20Assistance%20Systems.pdf)\n* 2019-[基于深度学习的雷达与相机传感器融合架构，用于目标检测](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8916629)\n* 2019-[基于深度学习的汽车雷达和相机3D目标检测](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8904867)\n* 2020-[YOdar不确定性驱动的传感器融合，用于结合相机和雷达传感器进行车辆检测](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03320v2)\n* 2020-[雷达+RGB融合，用于自动驾驶中的鲁棒目标检测](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9191046)\n* 2020-[用于3D车辆检测的低层传感器融合，使用雷达距离-方位热图和单目图像](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2020\u002Fhtml\u002FKim_Low-level_Sensor_Fusion_Network_for_3D_Vehicle_Detection_using_Radar_ACCV_2020_paper.html)\n* 2020-[用于障碍物检测的毫米波雷达与视觉传感器的空间注意力融合](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F4\u002F956)\n\n* 2019年—[RRPN：用于自动驾驶车辆目标检测的雷达区域建议网络](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8803392)\n* 2020年—[CenterFusion：基于中心点的雷达与相机融合3D目标检测方法](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.04841)\n* 2020年—[自动驾驶车辆中用于联合目标检测与距离估计的雷达-相机传感器融合](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08428)\n* 2020年—[GRIF Net：用于从雷达点云和单目图像中实现鲁棒3D目标检测的门控感兴趣区域融合网络](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9341177)\n* 2021年—[milliEye：一种轻量级毫米波雷达与相机融合系统，用于鲁棒目标检测](http:\u002F\u002Faiot.ie.cuhk.edu.hk\u002Fpapers\u002FmilliEye.pdf)\n\n* 2021年—[基于单目视觉相机和双低功耗4D毫米波雷达的道路车辆3D检测与跟踪](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564904)\n\n* 2011年—[将毫米波雷达与单目视觉传感器集成用于道路障碍物检测应用](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F11\u002F9\u002F8992)\n* 2017年—[雷达-红外传感器融合方法在目标检测中的比较分析](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8204237?casa_token=Iclafffn7ZEAAAAA:0zfHdvHe2VXd3mQOW_AMjexp-fL4zfzyTZ7CKesXw7jnKEuWe0Ty-akJBW4HYg8pkfJtzfPhz5k)\n* 2019年—[通过雷达与相机传感器的协同融合进行人员跟踪](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8917238)\n* 2019年—[基于毫米波雷达与相机传感器融合的DNN-LSTM目标跟踪方法](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9058168)\n* 2020年—[基于雷达与单目相机融合的无人机自主避障](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341432?casa_token=hZJ5haSOVqEAAAAA:UoczK2JQxnrkU_rdXKERsm6oVDtLwtembw1iB-dBrrKuZqfbDjgSA4phgCNRI0H-cxGuj_d8NqY)\n* 2019年—[通过与相机融合扩展毫米波雷达跟踪与检测的可靠性](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8844649)\n* 2019年—[通过雷达与相机传感器的协同融合进行人员跟踪](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917238)\n* 2019年—[基于毫米波雷达与相机融合的目标检测算法](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917504)\n* 2019年—[使用毫米波雷达与相机传感器融合的基于DNN-LSTM的目标跟踪方法](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9058168?casa_token=-51KkM4RDJUAAAAA:DiM3jG_heIcHkxgsAmrE5ewfVRSrqbp24ChSzRAKYY-nXboD9KZKOp0G1Jl4B0PKi53UnhBfZCI)\n* 2020年—[用于智能交通的路侧相机-雷达感知融合系统](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9337488?casa_token=4Wws9Vyc4UcAAAAA:ktkw199jc7Wtlk2CjHDxOPMQnPCTWINWTZUqEuuTFuL3TsC-P3_U7wqSEQfOPNj72oNt98KFATY)\n* 2020年—[在存在漏检的情况下对弱势道路使用者的多传感器协同跟踪](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F17\u002F4817)\n* 2020年—[基于路侧雷达和相机的鲁棒目标检测与跟踪算法](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F21\u002F4\u002F1116)\n\n* 2017年—[利用扩展目标方法进行车辆跟踪：一种融合雷达和激光的方法](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7989029)\n* 2019年—[利用GNSS自动估算汽车雷达数据中弱势道路使用者的真实位置](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8726801)\n* 2019年—[学习穿透雾霾：基于雷达的人体检测以应对恶劣天气条件](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8870954)\n* 2020年—[LiRaNet：基于时空雷达融合的端到端轨迹预测](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00731)\n* 2020年—[RadarNet：利用雷达实现对动态物体的鲁棒感知](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.14366)\n\n* 2016年—[多传感器融合与分类用于移动目标检测与跟踪](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7283636)\n* 2020年—[高维截锥PointNet用于从相机、LiDAR和雷达中进行3D目标检测](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9304655)\n* 2020年—[不看雾却能看清雾中景象：在未见的恶劣天气条件下进行深度多模态传感器融合](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9304655)\n* 2021年—[用于3D目标检测的雷达体素融合](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F11\u002F12\u002F5598)\n\n---\n\n\n\n## 弱监督\n* 2022年—一种用于机器人环境感知的新雷达点云生成方法 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9823311)\n* 2022年—看、辐射并学习：通过无线电-视觉对应关系进行自监督定位 __`Arxiv`__; __`Simulation`__; __`SpatialContrastive`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.06424)\n* 2021年—R4Dyn：探索雷达用于动态场景的自监督单目深度估计 __`3DIMPVT`__; __`nuScenes`__; __`SSL`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.04814)\n* 2021年—RODNet：一种由相机-雷达融合目标3D定位交叉监督的实时雷达目标检测网络 __`IJSTSP`__; __`CRUW`__; __`ConfMap`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9353210); [代码](https:\u002F\u002Fgithub.com\u002Fyizhou-wang\u002FRODNet); [视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UZbxI4o2-7g)\n* 2020年—RSS-Net：采用FMCW雷达的弱监督多类语义分割 __`IV`__; __`Oxford`__; __`PoseChain`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9304674)\n* 2020年—将雷达数据扭曲为相机图像，用于汽车应用中的跨模态监督 __`TVT`__; __`Velocity`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.12809)\n* 2020年—用于FMCW雷达中弱势道路使用者检测的弱监督深度学习方法 __`ITSC`__; __`Tracking`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9294399)\n* 2020年—雷达作为教师：利用雷达标签进行弱监督车辆检测 __`ICRA`__; __`CoTeaching`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9196855)\n\n---\n\n## 深度估计\n* 2023年——基于相机图像和毫米波雷达点云的深度估计 __`CVPR`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FSingh_Depth_Estimation_From_Camera_Image_and_mmWave_Radar_Point_Cloud_CVPR_2023_paper.html)\n* 2022年——RVMDE：用于机器人技术的雷达验证单目深度估计 __`Arxiv`__; __`nuScenes`__ [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05265); [代码](https:\u002F\u002Fgithub.com\u002FMI-Hussain\u002FRVMDE)\n* 2021年——语义引导的雷达-视觉融合用于深度估计和目标检测 __`BMVC`__; __`nuScenes`__; __`SemanticJoint`__; [论文](https:\u002F\u002Fbiblio.ugent.be\u002Fpublication\u002F8713974) \n* 2021年——利用深度序数回归网络从单目图像和稀疏雷达进行深度估计 __`ICIP`__; __`nuScenes`__; __`DORN`__ ; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9506550); [代码](https:\u002F\u002Fgithub.com\u002Flochenchou\u002FDORN)\n* 2021年——R4Dyn：探索雷达在动态场景自监督单目深度估计中的应用 __`3DIMPVT`__; __`nuScenes`__; __`SSL`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.04814)\n* 2021年——用于深度补全的雷达-相机像素深度关联 __`CVPR`__; __`nuScenes`__; __`MER`__ ;  [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fhtml\u002FLong_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.html); [代码](https:\u002F\u002Fgithub.com\u002Flongyunf\u002Frc-pda)\n* 2020年——从单目图像和稀疏雷达数据进行深度估计 __`IROS`__; __`nuScenes`__; __`TwoStage`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9340998);  [代码](https:\u002F\u002Fgithub.com\u002Fbrade31919\u002Fradar_depth)\n* 2020年——用于三维深度重建的相机-雷达融合 __`IV`__; __`RadarRA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9304559); [视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T9c75fmmxyQ)\n\n---\n\n## 自主导航运动估计\n* 2022年——一种可信且鲁棒的汽车雷达自主导航运动估计方法 __`RAL`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9743799)\n* 2021年——通过雷达速度因子实现低成本毫米波雷达的三维自主导航运动估计，用于位姿图SLAM __`ICRA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9495184)\n* 2020年——milliEgo：基于深度传感器融合的单芯片毫米波辅助自主导航运动估计 __`SenSys`__; [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3384419.3430776); [代码](https:\u002F\u002Fgithub.com\u002FChristopherLu\u002FmilliEgo)\n* 2020年——用于视觉退化环境的雷达-惯性自主导航速度估计 __`ICRA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9196666)\n* 2018年——在多样且挑战性条件下使用毫米波雷达进行精确的自主导航运动估计 __`ICRA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8460687)\n* 2014年——利用多普勒雷达进行瞬时自主导航运动估计 __`ICRA`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6907064)\n\n---\n\n## 速度估计\n* 2023年——隐藏的瑰宝：利用跨模态监督学习4D雷达场景流 __`CVPR`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.00462)\n* 2022年——用于汽车雷达目标检测网络的自监督速度估计 __`nuScenes`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03146)\n* 2022年——利用4D汽车雷达进行自监督场景流估计 __`RAL`__; __`4DRadar`__; __`FlowNet`__; [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.01137); [代码](https:\u002F\u002Fgithub.com\u002FToytiny\u002FRaFlow)\n* 2021年——通过雷达-相机融合获得完整的雷达速度信息 __`ICCV`__; __`nuScenes`__; __`OpticalFlow`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fhtml\u002FLong_Full-Velocity_Radar_Returns_by_Radar-Camera_Fusion_ICCV_2021_paper.html)\n* 2021年——针对不确定动态环境的3D雷达速度图 __`IROS`__; __`Bayesian`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636019); [代码](https:\u002F\u002Fgithub.com\u002FRansML\u002FBDF)\n* 2020年——基于RLS的扩展雷达跟踪瞬时速度估计器 __`IROS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341127)\n* 2018年——使用单个高分辨率雷达传感器进行瞬时实际运动估计 __`Nonlinear`__ [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8443553)\n* 2014年——利用双多普勒雷达对任意物体进行瞬时全运动估计 __`DualRadar`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856449)\n* 2013年——利用多普勒雷达对车辆进行瞬时侧向速度估计 __`MultiPts`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6641086)\n\n\n---\n\n## 跟踪\n\n### 神经网络\n* 2022年——基于深度学习的细胞级目标跟踪、速度估计及传感器数据随时间推演的方法\n [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.06126)\n* 2022年——利用雷达感知中的时间关系进行自动驾驶 __`CVPR`__; __`Oxford`__; __`Attention`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html)\n* 2021年——利用循环卷积网络对稀疏点云进行端到端在线多目标跟踪 [论文](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-86380-7_33)\n* 2021年——CFTrack：基于中心的雷达与相机融合3D多目标跟踪 __`IV`__; __`CenterFusion+Track`__; __`nuScenes`__;  [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.05150); [视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_vuO19L6L0Q)\n\n### 贝叶斯滤波\n* 2021年——基于道路地图辅助的GM-PHD雷达多车辆跟踪滤波器 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9403944)\n* 2021年——Bayesradar：用于改进和可靠雷达目标分类的贝叶斯度量-卡尔曼滤波学习 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9596290)\n* 2021年——自动驾驶领域中，由扩展目标跟踪辅助的自适应多假设聚类方法应用于雷达 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9466233)\n* 2021年——一种基于图的检测前跟踪算法，用于车载雷达目标检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9276431)\n* 2020年——BAAS：用于人工智能应用的雷达传感器数据目标标注的贝叶斯跟踪与融合辅助系统 __`RadarConf`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9266698)\n* 2020年——基于RLS的瞬时速度估计器，用于扩展目标雷达跟踪 __`IROS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341127)\n* 2021年——基于车载雷达的数据区域关联车辆跟踪 __`TITS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9525315)\n* 2020年——利用空间分辨的微多普勒特征进行扩展目标跟踪 __`TIV`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9247291)\n* 2019年——自动驾驶应用中，由扩展目标跟踪辅助的自适应聚类方法应用于雷达 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8916658)\n* 2018年——用于数据关联和多目标跟踪的3D雷达RFCM __`ICASSP`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8461917)\n* 2018年——自动驾驶领域的分类辅助跟踪 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8547138)\n* 2015年——使用高分辨率多普勒雷达对扩展目标进行跟踪 __`TITS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7355362)\n* 2010年——基于道路强度的地图构建，利用概率假设密度滤波器处理雷达测量数据 __`TSP`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5677613)\n\n\n### 建模\n* 2022年——面向扩展目标的车载雷达探测随机建模的数据驱动方法 __`GeMic`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9783497)\n* 2021年——用于衡量自动驾驶雷达感知从仿真到现实差距的多层方法 __`IV`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564521)\n* 2021年——基于学习的车载雷达分层截断测量模型扩展目标跟踪 __`JSTSP`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9351598)\n* 2020年——使用车载雷达的分层截断测量模型进行扩展目标跟踪 __`ICASSP`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9054614)\n\n\n\n---\n\n## 预测\n* 2021年——利用车载雷达和循环神经网络预测车辆行为 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9520242)\n* 2021年——基于深度学习的去中心化帧间轨迹预测，适用于车载雷达的二值距离-角度地图 __`TVT`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9437958)\n* 2020年——LiRaNet：基于时空雷达融合的端到端轨迹预测 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00731)\n* 2020年——FISHING Net：网格中语义热图的未来推理 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09917)\n\n---\n\n## 占用栅格地图\n* 2022年——在保留远距离感知与穿透能力的同时，以激光雷达为监督的雷达占用预测 __`RAL`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9689995)\n* 2020年——穿越烟雾：利用低成本毫米波雷达进行鲁棒室内建图 __`MobiSys`__；[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3386901.3388945)\n* 2019年——通过语义分割从稀疏雷达簇中学习占用栅格，实现道路场景理解 __`ICCV`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCVW_2019\u002Fhtml\u002FCVRSUAD\u002FSless_Road_Scene_Understanding_by_Occupancy_Grid_Learning_from_Sparse_Radar_ICCVW_2019_paper.html)\n* 2019年——或许未知：雷达的深度逆传感器建模 __`ICRA`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8793263)\n* 2019年——利用深度雷达网络生成自动驾驶用占用栅格 __`ITSC`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8916897)\n* 2015年——车载雷达栅格地图表示 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7117922)\n\n### 点云地图\n* 2022年——来自毫米波雷达的高分辨率点云 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.09273)\n* 2020年——移除再恢复：利用多分辨率距离图像构建静态点云地图 __`IROS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9340856)\n\n\n---\n\n## 开放空间分割\n* 2022年——用于多任务学习的原始高清雷达 __`CVPR`__；__`数据集`__；[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FRebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html)\n* 2022年——可变形雷达多边形：一种轻量级且可预测的占用表示，用于短程避障 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.01442)\n* 2022年——利用雷达为自动驾驶车辆估计可行驶区域 __`TVT`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9740418)\n* 2021年——PolarNet：利用车载雷达在极坐标域加速深度开放空间分割 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03387)\n* 2020年——利用车载雷达进行深度开放空间分割 __`数据集`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9299052)\n* 2018年——基于高分辨率雷达的占用栅格建图与自由空间检测 [论文](https:\u002F\u002Fwww.scitepress.org\u002Fpapers\u002F2018\u002F66673\u002F)\n\n---\n\n## 场景理解\n* 2020年——车载雷达3D占用栅格上的语义分割 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9229096)\n* 2020年——针对车载雷达的统计图像分割与区域分类方法 __`EuRAD`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9337399)\n* 2019年——车载雷达的场景理解 __`TIV`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8911477)\n* 2018年——雷达点云上的语义分割 __`FUSION`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8455344)\n\n---\n\n## 地点识别\n* 2024-TransLoc4D：基于Transformer的4D雷达地点识别 __`CVPR`__\n* 2022-AutoPlace：单芯片车载雷达的鲁棒地点识别 __`ICRA`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811869)；[代码](https:\u002F\u002Fgithub.com\u002Framdrop\u002FAutoPlace)\n* 2021-无监督雷达地点识别中的对比学习 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9659335)\n* 2021-雷达转激光雷达：通过联合学习实现异构地点识别 [论文](https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffrobt.2021.661199\u002Ffull)\n* 2021-基于深度嵌入学习的雷达视频无监督地点识别 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06703)\n* 2020-环顾四周：具有学习到的旋转不变性的序列式雷达地点识别 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9109951)\n* 2020-MulRan 城市地点识别用多模态距离数据集 __`ICRA`__；__`数据集`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9197298)\n\n---\n\n## 惯性导航与SLAM\n### 惯性导航\n* 2022-基于SE(3)的雷达惯性导航：采用恒定加速度运动先验和极坐标测量模型 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.05956)\n* 2022-Fast-MbyM：利用傅里叶变换的平移不变性实现高效且精确的雷达惯性导航 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812063) __`ICRA`__；[代码](https:\u002F\u002Fgithub.com\u002Fapplied-ai-lab\u002Ff-mbym)\n* 2021-结合概率估计与无监督特征学习的雷达惯性导航 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.14152)\n* 2021-基于SE(3)的雷达惯性导航：采用恒定速度运动先验 __`RAL`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9463737)\n* 2022-周而复始：在雷达惯性导航中利用恒曲率运动约束 __`RAL`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9808131)\n* 2021-我们是否需要在旋转雷达导航中补偿运动畸变和多普勒效应 __`RAL`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9327473)；[代码](https:\u002F\u002Fgithub.com\u002Fkeenan-burnett\u002Fyeti_radar_odometry)\n* 2021-一种专为扫描式及车载雷达设计的基于正态分布变换的雷达惯性导航 __`ICRA`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9561413)\n* 2021-BFAR——改进雷达惯性导航估计的有界虚警率检测器 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.09669)\n* 2021-CFEAR Radarodometry——用于高效准确雷达惯性导航的保守滤波 __`IROS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636253)\n* 2021-面向车载雷达的连续时间雷达惯性导航 __`IROS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636014)\n* 2021-面向高效准确雷达惯性导航的定向表面点 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.09994)\n* 2020-PhaRaO：利用相位相关直接进行雷达惯性导航 __`ICRA`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9197231)\n* 2019-以动掩静：从位姿信息中学习无干扰的雷达惯性导航 __`CoRL`__；[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv100\u002Fbarnes20a)\n\n### SLAM\n* 2022-CorAl：利用微分熵实现多样化环境中鲁棒的雷达与激光雷达感知 __`RAS`__；[论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0921889022000768)\n* 2022-我们是否已准备好让雷达取代激光雷达进行全天候地图构建与定位 [论文](http:\u002F\u002F128.84.4.18\u002Fabs\u002F2203.10174)\n* 2021-SERALOC：基于语义标注的雷达点云SLAM __`ITSC`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564693)\n* 2021-RaLL：使用可微分测量模型在激光雷达地图上实现端到端雷达定位 __`TITS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9370010)\n* 2021-利用共享嵌入改进雷达在激光雷达地图上的定位 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.10000)\n* 2021-针对恶劣环境条件，使用轻量级、低成本毫米波雷达进行跨模态表示对比学习以辅助导航 __`RAL`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9362209)\n* 2021-RadarLoc：学习在FMCW雷达中重新定位 __`ICRA`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9560858)\n* 2021-雷达SLAM：一种适用于所有天气条件的鲁棒SLAM系统 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05347)\n* 2020-RadarSLAM：基于雷达的全天候大规模SLAM __`IROS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341287)\n* 2020-基于测距传感器与航拍影像的自监督定位 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.02108)\n* 2020-RSL-Net：从地面雷达定位卫星图像 __`RAL`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8957240)\n* 2020-暗中摸索：学习预测用于雷达惯性导航估计和度量定位的鲁棒关键点 __`ICRA`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9196835)\n* 2020-雷达对激光雷达：基于先前激光雷达地图的度量雷达定位 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9303291)\n* 2020-kRadar++：粗细结合的FMCW扫描式雷达定位 [论文](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F21\u002F6002)\n* 2020-被劫持的雷达：利用旋转不变度量学习实现拓扑雷达定位 __`ICRA`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9196682)\n* 2020-一种融合低成本IMU、GNSS、雷达、相机和激光雷达的可扩展鲁棒车辆状态估计框架 __`IROS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9341419)\n* 2005-一种适用于毫米波雷达多视距特征的增强状态SLAM公式 __`IROS`__；[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1545232)\n\n\n---\n\n## 汽车SAR\n* 2022年——面向汽车应用的运动目标合成孔径雷达成像 __`EuRAD`__; __`Bosch`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9784564)\n* 2022年——基于雷达运动估计的汽车SAR性能分析 __`GM`__;  [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.10406)\n* 2022年——汽车MIMO SAR成像中的残余运动补偿 __`RadarConf`__; __`Huawei`__;  [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9764310)\n* 2022年——用于汽车前视SAR成像的快速简易处理器 __`RadarConf`__; __`Huawei`__;  [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9764234)\n* 2021年——城市车联网场景下的协作式合成孔径雷达 __`Huawei`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9376348)\n* 2021年——用于驾驶环境高分辨率成像的导航辅助汽车SAR __`Huawei`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9363205)\n* 2021年——面向自动驾驶功能的成像雷达 __`Continental`__; [论文](https:\u002F\u002Fwww.cambridge.org\u002Fcore\u002Fjournals\u002Finternational-journal-of-microwave-and-wireless-technologies\u002Farticle\u002Fimaging-radar-for-automated-driving-functions\u002F81B92D1CCF86309E8A354783A343861E)\n* 2021年——MIMO-SAR：用于自动驾驶中毫米波FMCW雷达的分层高分辨率成像算法 __`TVT`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9465646)\n* 2020年——利用毫米波雷达生成3D点云 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3432221)\n* 2020年——用于动态目标半监督学习的高分辨率雷达数据集 __`CVPRW`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2020\u002Fhtml\u002Fw6\u002FMostajabi_High-Resolution_Radar_Dataset_for_Semi-Supervised_Learning_of_Dynamic_Objects_CVPRW_2020_paper.html)\n\n### ISAR\n* 2022年——利用逆合成孔径雷达图像对汽车目标进行分类 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9695280)\n* 2021年——逆合成孔径雷达成像：历史回顾与现状综述 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9513303\u002F)\n\n---\n \n## 人体活动识别\n\n### 点云\n* 2022年——跨视觉-RF步态重识别：基于低成本RGB-D相机和毫米波雷达 [论文](https:\u002F\u002Fwww.research.ed.ac.uk\u002Fen\u002Fpublications\u002Fcross-vision-rf-gait-re-identification-with-low-cost-rgb-d-camera)\n* 2022年——Tesla-Rapture：基于毫米波雷达稀疏点云的轻量级手势识别系统 __`TMC`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9720163)\n* 2021年——m-Activity：通过毫米波雷达实现准确且实时的人体活动识别 __`ICASSP`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9414686)\n* 2020年——通过毫米波感知在智能家居场景中实现实时手臂手势识别 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3432235)\n* 2019年——RadHAR：基于毫米波雷达生成的点云进行人体活动识别 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3349624.3356768)\n\n### 微多普勒\n* 2022年——基于注意力机制的双流视觉Transformer用于雷达步态识别 __`ICASSP`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9746565)\n* 2021年——利用有限的雷达微多普勒特征进行人体运动识别 __`TGRS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9222330)\n* 2018年——基于多站式微多普勒特征，使用深度卷积神经网络进行人员识别和步态分类 __`TGRS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8307105)\n\n### RD\n* 2021年——RadarNet：利用微型雷达传感器的高效手势识别技术 __`CHI`__; [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3411764.3445367)\n* 2016年——与Soli互动：探索射频频谱中的细粒度动态手势识别 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F2984511.2984565); [视频](https:\u002F\u002Fyoutu.be\u002FZSkl9zoNZRY)\n* 2015年——用于手势感知的短程FMCW单脉冲雷达 __`RadarConf`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7131232)\n\n\n### 领域适应\n* 2022年——利用无线电信号进行人体感知的无监督学习 __`WACV`__; [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2022\u002Fhtml\u002FLi_Unsupervised_Learning_for_Human_Sensing_Using_Radio_Signals_WACV_2022_paper.html)\n* 2022年——利用边缘差异不一致性实现FMCW雷达配置间的无监督领域适应 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.04588)\n* 2022年——mTransSee：通过迁移学习实现环境无关的毫米波感知手势识别 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3517231)\n* 2022年——在边缘端实现少量样本用户自定义的基于雷达的手势识别 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9722880)\n* 2021年——迈向基于毫米波信号的领域无关、实时手势识别 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.06195)\n* 2021年——利用雷达微多普勒特征进行半监督人体活动识别 __`TGRS`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9467531)\n* 2021年——为少量样本的基于雷达的人体活动识别提供有监督领域适应 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9558812)\n* 2021年——迈向基于雷达、无需源数据的跨环境人体活动识别 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9551721)\n* 2021年——基于微多普勒特征的人体活动分类的持续学习 __`GRSL`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9319850)\n\n### 分布式\n* 2022年——利用雷达数据域进行空间分布式节点分类 [论文](https:\u002F\u002Fresearch.tudelft.nl\u002Fen\u002Fpublications\u002Fexploiting-radar-data-domains-for-classification-with-spatially-d)\n\n\n\n---\n\n## 雷达-音频\n### 语音恢复\n* 2022年——Wavesdropper：通过商用毫米波设备检测穿墙人声 [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534592)\n* 2022年——Radio2Speech：从射频信号中高质量恢复语音 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.11066)\n* 2021年——mmPhone：通过毫米波表征的压电效应监听扬声器声音 __`INFOCOM`__; [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9796806)\n\n### 声带\n* 2022年——利用MIMO FMCW雷达检测多目标时变声带振动 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9765794)\n* 2021年——VocalPrint：一种基于毫米波的直接声带感知系统，用于安全认证 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9444641)\n\n### 分离\n* 2022年——RadioSES：基于毫米波的音频增强与分离系统 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.07092)\n* 2021年——Wavoice：融合毫米波和音频信号的抗噪多模态语音识别系统 __`SenSys`__; [论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3485730.3485945)\n\n\n---\n\n## 天气影响\n\n### 效果\n* 恶劣天气条件对自动驾驶汽车的影响：雨、雪、雾和冰雹如何影响自动驾驶汽车的性能 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8666747)\n* 穿透灰尘和水汽：用于采矿应用的毫米波雷达传感器 [论文](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1002\u002Frob.20166)\n* 商用77 GHz车载雷达中降雨杂波检测分析 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8249138)\n* 全天候条件下智能地面车辆的感知系统：系统性文献综述 [论文](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F20\u002F22\u002F6532)\n* 恶劣天气条件下车载点云传感器的测试与验证 [论文](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F9\u002F11\u002F2341)\n* 自动驾驶汽车在雨天条件下的目标检测：现状与新兴技术综述 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9307324)\n* 恶劣天气条件下车载点云传感器的测试与验证 [论文](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F9\u002F11\u002F2341)\n* 雾天环境下ToF激光雷达会发生什么？ [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9121741)\n\n### 数据集\n* 牛津雾天 [代码]((https:\u002F\u002Fgithub.com\u002Fqiank10\u002FMVDNet))\n* DENSE \n* RADIATE \n* Boreas\n* K-Radar\n \u003C\u002Fbr>详情请参阅数据集部分。\n\n### 方法\n* 2022年——用于车辆检测的雷达-激光雷达融合中的模态无关学习 [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html)\n* 2021年——利用互补的激光雷达和雷达信号实现雾天环境下的鲁棒多模态车辆检测 [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fhtml\u002FQian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html)\t\n* 2020年——不“看见”雾却能穿透雾：在未见的恶劣天气中进行深度多模态传感器融合 [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FBijelic_Seeing_Through_Fog_Without_Seeing_Fog_Deep_Multimodal_Sensor_Fusion_CVPR_2020_paper.html); [代码](); [视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HPT4nsCkT5Q)\n* 2020年——利用毫米波雷达实现雾中高分辨率成像 [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FGuan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html); [代码](https:\u002F\u002Fgithub.com\u002FJaydenG1019\u002FHawkEye-Data-Code); [视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HPT4nsCkT5Q)\n\n---\n\n## 多径效应\n\n### 数据集\n* 2021年——雷达鬼影数据集——对车载雷达数据中鬼影物体的评估 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9636338); [代码](https:\u002F\u002Fgithub.com\u002Fflkraus\u002Fghosts)\n\n### 方法\n* 2021年——使用PointNets进行雷达数据中的异常检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9564730)\n* 2021年——基于规则的车载雷达数据快速杂波检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564776)\n* 2021年——通过多模态Transformer进行雷达鬼影目标检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9497756)\n* 2021年——利用基于点云的深度神经网络在3D雷达数据中检测鬼影目标 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9413247)\n* 2020年——使用机器学习检测车载雷达中的鬼影图像 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9294631)\n* 2020年——绕过街角：非视距检测与跟踪，野外场景下利用多普勒雷达 [论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FScheiner_Seeing_Around_Street_Corners_Non-Line-of-Sight_Detection_and_Tracking_In-the-Wild_Using_CVPR_2020_paper.html)\n* 2019年——利用深度学习识别车载场景中的鬼影移动目标 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8726704)\n* 2018年——不确定环境下的车载雷达多径传播 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8570016)\n\n\n\n---\n\n## 相互干扰\n### 方法\n* 2022年——用于车载雷达干扰检测与抑制的两阶段掩膜门控卷积神经网络模型 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9770068)\n* 2021年——用于车载雷达干扰抑制的资源高效深度神经网络 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9364355)\n* 2021年——用于车载雷达干扰抑制的DNN自编码器 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9413619)\n* 2021年——基于CFAR的FMCW车载雷达系统干扰抑制 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9541302)\n* 2021年——在车载FMCW雷达系统中使用小波去噪抑制相互干扰 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8946905)\n* 2020年——FMCW雷达中的干扰特性分析 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9266283)\n* 2020年——真实世界FMCW雷达信号的深度干扰抑制与去噪 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9114627); [视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DJY9Zk1p9-g)\n* 2019年——利用卷积神经网络对车载雷达进行复杂信号去噪和干扰抑制 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9011164)\n* 2018年——一种用于车载雷达干扰抑制的深度学习方法 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8690848)\n### 综述\n* 2022年——利用深度学习抑制干扰：当前方法与开放挑战 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9802083)\n* 2020年——自动驾驶中的雷达干扰抑制：探索主动策略 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9127843)\n* 2020年——车载雷达系统中的干扰：特性、抑制技术以及当前和未来的研究 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8828037)\n\n## 距离与多普勒单元迁移\n* 2021年——利用LFMCW车载雷达进行增强型高速运动目标检测的多普勒-距离处理 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9506841)\n* 2019年——宽带车载雷达中的距离与多普勒单元迁移 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8695853)\n\n## 发射-接收泄漏\n* 2022年——采用混合模拟与数字补偿技术抑制短程FMCW雷达中的泄漏和静止杂波 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9583883)\n* 2019年——天线互耦效应对MIMO雷达系统性能的影响 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8581509)\n\n## 波形分离不完善\n* 2020年——波形分离不完善的慢时MIMO-FMCW车载雷达检测 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9053892)","# awesome-radar-perception 快速上手指南\n\n`awesome-radar-perception` 并非一个可直接安装运行的软件库或工具包，而是一个**精选的雷达感知资源列表**。它汇集了自动驾驶和机器人领域所需的雷达数据集、检测算法、跟踪方法、传感器融合方案以及相关综述论文。\n\n本指南旨在帮助中国开发者快速理解该项目的结构，并高效获取所需的国内可用资源（如数据集、代码库和文献）。\n\n## 环境准备\n\n由于本项目主要是资源索引，无需特定的系统环境。但为了使用列表中链接的具体算法和数据集，建议准备以下基础开发环境：\n\n*   **操作系统**: Linux (推荐 Ubuntu 18.04\u002F20.04) 或 macOS\n*   **编程语言**: Python 3.8+ (大多数深度学习雷达算法基于 PyTorch 或 TensorFlow)\n*   **版本控制**: Git\n*   **依赖管理**: Conda 或 venv\n*   **网络环境**: 部分数据集托管在 GitHub 或国外服务器，建议配置科学上网环境或使用文中提供的国内镜像链接。\n\n## 安装步骤\n\n本项目无需通过 `pip` 或 `apt` 安装。获取资源的最佳方式是克隆仓库以获取完整的目录索引，或直接访问文中提供的特定链接。\n\n### 1. 克隆项目仓库\n获取最新的资源列表和本地文档：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FYi-Zhou\u002Fawesome-radar-perception.git\ncd awesome-radar-perception\n```\n\n### 2. 获取核心文献（含国内加速链接）\n作者 Yi Zhou 发表了一篇关于雷达感知的综述论文及配套幻灯片，这是入门该领域的关键资料。项目提供了阿里云盘的中国大陆加速下载链接：\n\n*   **综述论文**: [Towards Deep Radar Perception for Autonomous Driving](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F22\u002F11\u002F4208) (开放获取)\n*   **配套幻灯片 (41 页)**:\n    *   国际链接: [SlideShare](https:\u002F\u002Fwww.slideshare.net\u002FYiZhou66\u002Fslidesdeepradarperceptionforautonomousdrivingpdf)\n    *   **中国大陆加速链接 (推荐)**: [阿里云盘下载](https:\u002F\u002Fwww.aliyundrive.com\u002Fs\u002FCZ3SKqY3U4w)\n\n## 基本使用\n\n使用本项目的核心逻辑是：**根据任务需求 -> 查找对应分类 -> 访问具体数据集或代码库**。\n\n### 场景示例：寻找用于目标检测的 4D 雷达数据集\n\n假设你需要一个带有 3D 边界框标注的 4D 雷达数据集来训练检测模型：\n\n1.  **查阅目录**：在本地 `README.md` 或在线页面找到 `4D Radar Datasets` 章节。\n2.  **筛选资源**：\n    *   **TJ4DRadSet**: 由天津大学雷达实验室发布，包含长距检测和 TrackID 标注。\n        *   地址: `https:\u002F\u002Fgithub.com\u002FTJRadarLab\u002FTJ4DRadSet` (GitHub 源，国内访问通常较快)\n    *   **K-Radar**: 由 KAIST 发布，提供 RAD 张量和 3D 标注。\n        *   地址: `https:\u002F\u002Fgithub.com\u002Fkaist-avelab\u002FK-Radar`\n    *   **RADIal**: 提供原始 ADC 数据和点云，标注包含车辆点级信息和开放空间掩码。\n        *   地址: `https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FRADIal`\n3.  **下载与使用**：点击对应链接进入子项目仓库，按照子项目的具体指令下载数据并运行示例代码。\n\n### 场景示例：查找雷达信号处理工具箱\n\n如果你需要处理原始 ADC 数据或进行 MIMO 校准：\n\n1.  **查阅目录**：定位到 `Signal Processing` 章节。\n2.  **选择工具**：\n    *   **Radar Toolbox**: 查找通用的雷达信号处理工具集。\n    *   **MIMO Calibration**: 查找专门用于 MIMO 雷达校准的代码实现。\n3.  **集成开发**：将找到的开源代码库克隆到本地，作为你项目的前处理模块。\n\n### 特别提示：国内友好资源\n在浏览列表时，优先关注以下带有国内背景或托管在国内平台的资源，以获得更稳定的下载速度：\n*   **TJ4DRadSet**: 天津大学团队维护，GitHub 仓库活跃。\n*   **FloW Dataset \u002F USVInland**: 由 Orca Tech (国内公司) 提供，专注于水面漂浮物和内河 SLAM 任务。\n*   **综述幻灯片**: 务必使用提供的阿里云盘链接下载，避免国外网站加载缓慢。","某自动驾驶初创团队正在研发全天候感知系统，急需解决雨天激光雷达失效时的目标检测难题，计划引入毫米波雷达作为核心传感器。\n\n### 没有 awesome-radar-perception 时\n- **数据搜集如大海捞针**：工程师需花费数周在各大网站零星搜索雷达数据集，难以找到包含密集点云和精确轨迹标注的高质量数据（如 Radar Scenes 或 nuScenes），导致模型训练起步缓慢。\n- **算法选型缺乏依据**：面对去噪、超分辨率和聚类等多种信号处理任务，团队无法快速定位业界领先的开源工具箱，只能重复造轮子或试用不成熟的代码。\n- **忽视关键干扰因素**：由于缺乏对多径效应、互干扰等挑战的系统性认知，初期模型在复杂路况下误检率极高，且不知道有哪些现成的解决方案可参考。\n- **学术与工程脱节**：难以获取最新的综述论文和研讨会资料，导致技术路线规划滞后于前沿研究，增加了项目试错成本。\n\n### 使用 awesome-radar-perception 后\n- **一站式获取权威数据**：团队直接通过清单锁定了适合融合任务的 nuScenes 和提供点级标注的 Radar Scenes 数据集，将数据准备周期从数周缩短至两天。\n- **快速集成成熟算法**：利用列表中整理的 Detector 和 Denoising 工具包，迅速搭建了基线系统，并针对雨天场景引入了专门的去噪方案。\n- **系统性规避技术坑点**：参考\"Challenges\"章节中关于天气效应和多径效应的专题资源，提前在算法中加入了抗干扰模块，显著提升了恶劣天气下的鲁棒性。\n- **紧跟前沿技术演进**：通过关联的综述论文和幻灯片，团队快速掌握了深度雷达感知的最新趋势，优化了传感器融合架构，确保了技术方案的先进性。\n\nawesome-radar-perception 将原本分散孤立的雷达感知资源串联成完整的知识图谱，帮助开发者从繁琐的调研中解放出来，专注于核心算法的创新与落地。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FZHOUYI1023_awesome-radar-perception_c735be4b.png","ZHOUYI1023","Yi Zhou","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FZHOUYI1023_b87649b1.jpg","Interested in radar perception.",null,"zhouyi1023@tju.edu.cn","YiZhou37","https:\u002F\u002Fgithub.com\u002FZHOUYI1023",1824,336,"2026-04-09T09:52:12",1,"","未说明",{"notes":88,"python":86,"dependencies":89},"该项目是一个雷达感知领域的资源列表（Awesome List），主要收录了数据集、论文、工具箱和相关算法的链接，本身不是一个可直接运行的软件或模型库，因此 README 中未提供具体的操作系统、GPU、内存、Python 版本或依赖库等运行环境需求。用户需根据列表中引用的具体子项目（如特定的检测算法或数据集工具）去查阅其各自的安装说明。",[],[13,14,15,16],[92,93,94,95,96,97,98,99],"autonomous-driving","autonomous-vehicles","dataset","deep-learning","detection","fusion","radar","slam","2026-03-27T02:49:30.150509","2026-04-11T10:03:59.505146",[],[]]