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https://github.com/ZHOUYI1023/awesome-radar-perception
A curated list of radar datasets, detection, tracking and fusion
https://github.com/ZHOUYI1023/awesome-radar-perception
List: awesome-radar-perception
autonomous-driving autonomous-vehicles dataset deep-learning detection fusion radar slam
Last synced: 7 days ago
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A curated list of radar datasets, detection, tracking and fusion
- Host: GitHub
- URL: https://github.com/ZHOUYI1023/awesome-radar-perception
- Owner: ZHOUYI1023
- Created: 2021-04-23T06:50:33.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-04T12:02:52.000Z (6 months ago)
- Last Synced: 2024-05-22T03:00:34.771Z (6 months ago)
- Topics: autonomous-driving, autonomous-vehicles, dataset, deep-learning, detection, fusion, radar, slam
- Homepage:
- Size: 509 KB
- Stars: 1,220
- Watchers: 66
- Forks: 270
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Citation: CITATION.cff
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README
A curated list of radar datasets, detection, tracking and fusion.
Keep updating.
Author: Yi Zhou
Contact: [email protected]
đźš©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.
## [Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges](https://www.mdpi.com/1424-8220/22/11/4208)
## The 41-page slides associated with this paper: [Link](https://www.slideshare.net/YiZhou66/slidesdeepradarperceptionforautonomousdrivingpdf) ; [Link for China Mainland](https://www.aliyundrive.com/s/CZ3SKqY3U4w)
---
## Contents
Overview
- [Review](#Review-Papers)
- [Seminars and Workshops](#Seminars-and-Workshops)
Data Perspective:
- [Radar Datasets](#Radar-Datasets)
- [Radar Signature](#Radar-Signature)
- [Calibration](#Calibration)
- [Labelling](#Labelling)
- [Augmentation](#Data-Augmentation)
- [Simulation](#Simulator)
- [Generative Model](#Generative-Model)
- [Testing](#Testing)
Signal Processing:
- [Radar Toolbox](#Radar-Toolbox)
- [MIMO Calibration](#MIMO-Calibration)
- [Detector](#Detector)
- [Super Resolution](#Super-Resolution)
- [Clustering](#Clustering)
- [Denoising](#Denoising)
Applications:
- [TI Reference Designs](#TI-Reference-Designs)
- [Ego-Motion Estimation](#Ego-Motion-Estimation)
- [Velocity Estimation](#Velocity-Estimation)
- [Depth Estimation](#Depth-Estimation)
- [Object Detection](#Object-Detection)
- [Sensor Fusion](#Sensor-Fusion)
- [Weakly Supervised](#Weakly-Supervised)
- [Tracking](#Tracking)
- [Prediction](#Prediction)
- [Occupancy Grid Map](#Occupancy-Grid-Map)
- [Open Space Segmentation](#Open-Space-Segmentation)
- [Scene Understanding (Static Segmentation)](#Scene-Understanding)
- [Place Recognition](#Place-Recognition)
- [Odometry and SLAM](#Odometry-and-SLAM)
- [Automotive SAR](#Automotive-SAR)
- [Human Activity](#Human-Activity-Recognition)
- [Radar-Audio](#Radar-Audio)
Challenges:
- [Weather Effect](#Weather-Effects)
- [Multi Path Effect](#Multi-Path-Effect)
- [Mutual Interference](#Mutual-Interference)
- [Cell Migration](#Range-and-Doppler-Cell-Migration)
- [Tx-Rx Leakage](#Tx-Rx-Leakage)
- [Imperfect Waveform Separation](#Imperfect-Waveform-Separation)
---
## Radar Datasets
In my [review paper](https://www.mdpi.com/1424-8220/22/11/4208), there is a table with more detials.
### Conventional Radar Datasets for Autonomous Driving
| Dataset | Radar Type | Data Type| Annotation | Link |
| ---- |----| ---- | ---- | ---- |
| nuScenes | Continental ARS408 x5 | Sparse PC | 3D bbox, TrackID | [Website](https://www.nuscenes.org/) |
| DENSE| 77Ghz Long-Range Radar | Sparse PC | 3D bbox |[Website](https://www.uni-ulm.de/en/in/driveu/projects/dense-datasets) |
| PixSet| TI AWR1843| Sparse PC | 3D bbox, TrackID| [Website](https://leddartech.com/solutions/leddar-pixset-dataset/)|
| Radar Scenes | 77GHz Middle-Range Radar x4 | Dense PC |2D point-wise, TrackID| [Website](https://radar-scenes.com/)|
| Pointillism | 2 TI AWR 1443 | PC | 3D bbox | [Github](https://github.com/Kshitizbansal/pointillism-multi-radar-data) |
| Zendar SAR | SAR | ADC, RD, PC| Pointwise Mask of Moving Vehicle |[Github](https://github.com/ZendarInc/ZendarSDK) |
| Cooperative Radars | 77GHz Radar x 3 | PC | Trajctory from GNSS-RTK | [Website](https://ieee-dataport.org/documents/radar-measurements-two-vehicles-three-cooperative-imaging-sensors) |
| aiMotive| 77GHz LRR Radar x2(Front and Back) | PC | 3D bbox, TrackID | [Website](https://github.com/aimotive/aimotive_dataset)|
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.
### Pre-CFAR Datasets for Detection
| Dataset | Radar Type | Data Type| Annotation | Link |
| ---- |----| ---- | ---- | ---- |
| CRUW | TI AWR1843 Ultra Short Range | RA | Pointlevel Object |[Website](https://www.cruwdataset.org/home)|
| CARRADA | TI AWR1843 Short Range | RA,RD,RAD | Pointwise, 2D bbox, Mask | [Website](https://arthurouaknine.github.io/codeanddata/carrada)|
| RADDet | TI AWR1843 | RAD | 3D bbox for RAD tensor | [Github](https://github.com/ZhangAoCanada/RADDet) |
| RaDICaL | TI IWR1443 | ADC | 2D bbox | [Website](https://publish.illinois.edu/radicaldata/)|
| GhentVRU | TI AWR1243 Short Range | RAD | Segmentation Mask for VRUs| [Paper](https://ieeexplore.ieee.org/document/9294399) |
| RAMP-CNN | TI AWR 1843| ADC | 2D bbox | [Website](https://github.com/Xiangyu-Gao/Raw_ADC_radar_dataset_for_automotive_object_detection) |
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.
### 4D Radar Datasets
| Dataset | Radar Type | Data Type| Annotation | Link |
| ---- |----| ---- | ---- | ---- |
| Astyx Hires2019 | Astyx 6455 HiRes Middel Range| PC | 3D bbox|[Dateset](https://github.com/under-the-radar/radar_dataset_astyx)|
| View-of-Delft | ZF FRGen21 Short Range| PC | 3D bbox |[Website](https://intelligent-vehicles.org/datasets/view-of-delft/)|
| RADIal | Valeo Middel Range DDM | ADC,RAD,PC | Point-level Vehicle; Open Space Mask|[Github](https://github.com/valeoai/RADIal)|
| TJ4DRadSet | Oculii Eagle Long Range | PC | 3D bbox, TrackID| [Github](https://github.com/TJRadarLab/TJ4DRadSet) |
| K-Radar | Macnica RETINA | RAD |3D bbox, Track ID|[Github](https://github.com/kaist-avelab/K-Radar); [OpenReview](https://openreview.net/forum?id=W_bsDmzwaZ7) |
| ZF 4DRadar Dataset | ZF FRGen21 4D | 3D | | TBD [Github](https://github.com/ZF4DRadSet/ZF-4DRadar-Dataset) |
| ThermRad | Oculii Eagle | PC | 3D | TBD |
| MSC-RAD4R | Oculii Eagle | PC | SLAM | [Website](https://mscrad4r.github.io/home/) |
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.
### Specific Tasks
| Dataset | Radar Type | Task | Link |
| ---- |----| ---- | ---- |
| HawkEye | SAR | Static vehicle classification | [Website](https://jaydeng1019.github.io/HawkEye/)|
| PREVENTION | Conti ARS308 + SRR208 x2 | Trajectory Prediction | [Website](https://prevention-dataset.uah.es/)|
| SCORP | 76GHz | Open space segmentation | [Website](https://sensorcortek.ai/paper-and-datasets/) |
| Ghost | 77GHz long range *2 | Ghost object detection | [Github](https://github.com/flkraus/ghosts) |
| Solinteraction Data | Soli | Tangible interactions| [Github](https://github.com/tcboy88/solinteractiondata) |
| GROUNDED | Ground Penetrating Radar | Localization | [Website](https://lgprdata.com/)|
|FloW Dataset | TI AWR1843 | Floating waste detection | [Website](http://orca-tech.cn/datasets/FloW/Introduction) |
| OLIMP | UWB + Continental ARS404| Multi-sensor fusion for detection|[Website](https://sites.google.com/view/ihsen-alouani/datasets)|
| DeepSense 6G | Radar+Lidar+Camera+GPS | Beam prediction | [Website](https://deepsense6g.net/)|
| CTA | Radar+Camera | Interference analysis | [Website](https://edata.bham.ac.uk/801/) |
| Radar^2 | TI AWR1843 | Spy radar detection | [Website](https://ieee-dataport.org/documents/radar2) |
| Darting Pedestrians dataset | ZF FRGen21 Short Range | Darting pedestrians detection | [Website](https://intelligent-vehicles.org/datasets/darting-pedestrians-dataset/) |
| ODA | 24GHz | Obstacle detection and avoidance for drones | [Website](https://github.com/tudelft/ODA_Dataset) |
| Scattering Dataset | 77GHz | | [Website](https://www.fzd-datasets.de/rcs/)|
| Radar Clutter Dataset | 77GHz | Clutter detections | [Website](https://github.com/kopp-j/clutter-ds) |
| Interference Dataset | 77GHz | Interference | [Website](https://ieee-dataport.org/documents/raw-adc-data-fmcw-radar-77-ghz-interference#files) |
| OSDaR23 | Navtech Radar | Rail-specific object detection | [Website](https://github.com/DSD-DBS/raillabel) |
### Odometry and Localization
| Dataset | Radar Type | Task | Link |
| ---- |----| ---- | ---- |
| The Oxford Offroad Radar Dataset | Navtech Spinning Radar | Place Recognition | [Website](oxford-robotics-institute.github.io/oord-dataset) |
| Oxford Radar Robocar | Navtech Spinning Radar | Odometry, (Detection) | [Website](https://oxford-robotics-institute.github.io/radar-robotcar-dataset/); [Detection Annotation](https://github.com/qiank10/MVDNet) |
|RADIATE| Navtech Spinning Radar | Odometry, Detection, Tracking | [Website](http://pro.hw.ac.uk/radiate/doc/dataset/)|
| MulRan | Navtech Spinning Radar | Place Recognition |[Website](https://sites.google.com/view/mulran-pr/dataset)|
| Boreas | Navtech Spinning Radar| Long-term Odometry, Localization, Detection | [Website](https://www.boreas.utias.utoronto.ca/#/)|
| EU Long-term Dataset | Conti ARS 308 | Long-term SLAM | [Website](https://epan-utbm.github.io/utbm_robocar_dataset/)|
| ColoRadar | TI AWR2243 Cascade + AWR1843 |Odometry | [Website](https://arpg.github.io/coloradar/) |
| USVInland | TI AWR1843 | SLAM in inland waterways, Water segmentation| [Website](http://orca-tech.cn/datasets/USVInland/Introduction) |
| Endeavour Radar Dataset | Conti ARS 430 x5 | Odometry | [Website](https://gloryhry.github.io/2021/06/25/Endeavour_Radar_Dataset.html)|
| OdomBeyondVision | TI AWR1843 | Odometry | [Website](https://github.com/MAPS-Lab/OdomBeyondVision) |
### Gesture
| Dataset | Radar Type | Data Type | Task | Link |
| ---- |----| ---- | ---- | ---- |
| DopNet | 24GHz | Spectrogram | Gesture | [Website](http://dop-net.com/)|
| MCD-Gesture | 77GHz | RAD tensor |Gesture | [Website](https://github.com/DI-HGR/cross_domain_gesture_dataset)|
| DeepSoli | 60GHz | RD map | Gesture | [Website](https://github.com/simonwsw/deep-soli) |
| Pantomime | TI IWR1443 | PC | Gesture | [Dataset](https://zenodo.org/record/4459969) |
| MIMOGR | | RADT | Gesutre | [Website](https://github.com/Tkwer/Gesture-Recognition-Based-on-mmwave-MIMO-Radar) |
### Human Activity and Reconstruction
| Dataset | Radar Type | Data Type | Task | Link |
| ---- |----| ---- | ---- | ---- |
| Radar signatures of human activities | 5.8 GHz | ADC | Human activities | [Dataset](http://researchdata.gla.ac.uk/848/) |
| Ci4R human activity dataset | 77GHz & 24GHz & 10GHz |Spectrogram | Human activities | [Website](https://github.com/ci4r/CI4R-Activity-Recognition-datasets/) |
| RadHAR | 77GHz | Point Cloud | Human activities | [Website](https://github.com/nesl/RadHAR) |
| mRI | 77GHz| PC, RGBD camera, IMU | Human pose estimation | [Website](https://sizhean.github.io/mri)|
| mmBody | Arbe Phoenix 4D Radar | PC, RGBD | 3D body reconstruction | [Website](https://chen3110.github.io/mmbody/index.html) ||
| HuPR | 2 TI 1843 | RAD | Pose | [Github](https://github.com/robert80203/HuPR-A-Benchmark-for-Human-Pose-Estimation-Using-Millimeter-Wave-Radar) |
### Vital Sign
| Dataset | Radar Type | Data Type | Task | Link |
| ---- |----| ---- | ---- | ---- |
| Child Vital Sign | 60GHz| ADC | heart beat, respiration | [Dataset](https://figshare.com/s/936cf9f0dd25296495d3) |
| GUARDIAN Vital Sign | 24GHz | IQ | heart beat, respiration | [Dataset 1](https://figshare.com/articles/dataset/A_dataset_of_clinically_recorded_radar_vital_signs_with_synchronised_reference_sensor_signals/12186516?file=22515785) [Dataset 2](https://figshare.com/articles/dataset/A_dataset_of_radar-recorded_heart_sounds_and_vital_signs_including_synchronised_reference_sensor_signals/9691544?backTo=/collections/GUARDIAN_Vital_Sign_Data/4633958)|
| Multi-Person Localization and Vital Sign Estimation Radar Dataset | | | | [Dataset](https://ieee-dataport.org/open-access/multi-person-localization-and-vital-sign-estimation-radar-dataset) |
---
## Radar Toolbox
### Simulation
RadarSimPy: [Code](https://github.com/rookiepeng/radarsimpy);
Virtual Radar: [Code](https://github.com/chstetco/virtualradar);
MaxRay: [Paper](https://arxiv.org/abs/2112.01751)
### TI Signal Processing SDK:
RaDICaL's Toolbox: [SDK](https://github.com/moodoki/radical_sdk);
PyRapid: [SDK](http://radar.alizadeh.ca);
OpenRadar : [SDK](https://github.com/presenseradar/openradar);
Pymmw: [SDK](https://github.com/m6c7l/pymmw);
Open radar initiative: [SDK](https://github.com/openradarinitiative);
RADIal's Emptyband-DDM Script: [Code](https://github.com/valeoai/RADIal/tree/main/SignalProcessing)
### Official SDK:
NXP Premium Radar SDK: [Link](https://www.nxp.com/design/automotive-software-and-tools/premium-radar-sdk-advanced-radar-processing:PREMIUM-RADAR-SDK);
TI mmWAVE Studio: [Link](https://www.ti.com/tool/MMWAVE-STUDIO);
TI Toolbox: [Link](https://dev.ti.com/tirex/explore/node?node=AHJY4qNCowO17wH-P2ICKQ);
Matlab Radar Toolbox: [Link](https://uk.mathworks.com/products/radar.html)
### Data Capturing:
TI Radar and Camera in Python:[Code](https://github.com/yizhou-wang/cr-data-collector);
Ainstein Radar ROS Node: [ROS Node](https://github.com/AinsteinAI/ainstein_radar);
Continental ARS 408 ROS Node: [ROS Node](https://gitlab.com/ApexAI/autowareclass2020/-/tree/master/code/src/09_Perception_Radar/Radar-Hands-On-WS);
TI mmWave ROS Driver: [Guide](https://dev.ti.com/tirex/explore/node?node=ADINBw2NDaxb6JeW7V-lMQ__VLyFKFf__LATEST&search=ROS);
RaDICaL's TI ROS Node: [ROS Node](https://github.com/moodoki/iwr_raw_rosnode);
UoA's TI ROS Package: [ROS Node](https://github.com/radar-lab/ti_mmwave_rospkg)
---
## Seminars and Workshops
* 2021 ICRA Radar Perception for All-Weather Autonomy [[Website]](https://sites.google.com/view/radar-robotics/home)
* 2021 ICASSP Recent Advances in mmWave Radar Sensing for Autonomous Vehicles [[Website]](https://www.2021.ieeeicassp.org/Papers/ViewSession_MS.asp?Sessionid=1280)
* Radar in Action Series by Fraunhofer FHR [[Video]](https://www.youtube.com/hashtag/radarinaction)
* IEEE AESS Virtual Distinguished Lecturer Webinar Series [[Website]](https://ieee-aess.org/activities/educational-activities/distinguished-lecturers)
* Journal of Radar Webinar Series (in Chinese) [[Video]](https://space.bilibili.com/1288394672)
* Markus Gardill: Automotive Radar – An Overview on State-of-the-Art Technology [[Video]](https://www.youtube.com/watch?v=P-C6_4ceY64&t=2416s)
* Markus Gardill: Automotive Radar – A Signal Processing Perspective on Current Technology and Future Systems [[Video]](https://www.youtube.com/watch?v=IxoPYhXY30k&t=11s)[[Slides]](https://cloud.gardill.net/s/tjoSLSB7fXWTEBb)
* Francesco Fioranelli: Radar Old but Gold- current research challenges and activities in radar micro-Doppler signatures [[Video]](https://www.youtube.com/watch?v=ysL6rk-4L9o&list=PLa5-fgjZm9MtJBtb6M3YplIDSdgr1m94n&index=3)
* Andrej Karpathy from Tesla: [[Video]](https://www.youtube.com/watch?v=g6bOwQdCJrc)
* Ole Schumann: Radar Perception for Automated Driving – Data and Methods [[Video]](https://www.youtube.com/watch?v=UQL7_Zy2Kjg&t=73s)
* Sani Ronen from Arbe: Using AI layer to transform HR radar into insights for Autonomous Driving applications [[Video]](https://www.youtube.com/watch?v=TtJW-c02YH8)
* Stefan Haag: Co-Development of Automatic Annotation for ML and Sensor Fusion Improvement System [[Video]](https://www.youtube.com/watch?v=ANbmXg2TxlE)
* Arthur Ouaknine: Deep Learning & Scene Understanding for autonomous vehicle [[Video]](https://www.youtube.com/watch?v=sOOvnTCnhPg)
* Paul Newman: The Road to Anywhere-Autonomy [[Video]](https://www.youtube.com/watch?v=MzYQMRG9HW0)
* Jaime Lien: Soli: Millimeter-wave radar for touchless interaction [[Video]](https://www.youtube.com/watch?v=JFr8Whnx630)
* Accelerating end-to-end Development of Software-Defined 4D Imaging Radar [[Videp]](https://www.youtube.com/watch?v=QPoj1zM2vCs)
* Radar-Imaging - An Introduction to the Theory Behind [[Video]](https://www.youtube.com/watch?v=ej2smyTZLHE)
* NXP - Radar Experts Discuss the Evolution of Automotive Radar [[Video]](https://www.youtube.com/watch?v=RfJiiSlesyE&t=347s)
* Need to Successfully Design a Milimeter-Wave Automotive Radar Antenna? [[Video]](https://www.youtube.com/watch?v=0lK8qJSWY_c)
* Webinar SAR Imaging using Ancortek’s Software Defined Radars [[Video]](https://www.youtube.com/watch?v=BMSVvQJYCIs)
* TI: Managing interference in FMCW radar systems [[Video]](https://training.ti.com/managing-interference-fmcw-radar-systems)
---
## Review Papers
Radar Hardware:
* [Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band](https://ieeexplore.ieee.org/document/6127923)
* [Radar-on-Chip/in-Package in Autonomous Driving Vehicles and Intelligent Transport Systems](https://ieeexplore.ieee.org/abstract/document/8830483)
* [Antenna Concepts for Millimeter-Wave Automotive Radar Sensors](https://ieeexplore.ieee.org/document/6165323)
* [System Performance of a 79 GHz High-Resolution 4D Imaging MIMO Radar With 1728 Virtual Channels](https://ieeexplore.ieee.org/abstract/document/9866614)
Radar Signal Processing:
* General: [The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions](https://ieeexplore.ieee.org/document/8828025/)
* Signal Processing: [Automotive Radar Signal Processing: Research Directions and Practical Challenges](https://ieeexplore.ieee.org/document/9369027)
* Signal Processing: [Automotive Radars A review of signal processing techniques](https://ieeexplore.ieee.org/abstract/document/7870764)
* Signal Processing: [Advances in Automotive Radar
A framework on computationally efficient high-resolution frequency estimation](https://ieeexplore.ieee.org/abstract/document/7870737)
* MIMO: [MIMO Radar for Advanced Driver-Assistance Systems and Autonomous Driving: Advantages and Challenges](https://ieeexplore.ieee.org/document/9127853)
* DOA: [Calibration and Direction-of-Arrival Estimation of mm-Wave Radars: A Practical Introduction](https://ieeexplore.ieee.org/abstract/document/9099537)
* Digital Radar: [High-Performance Automotive Radar: A Review of Signal Processing Algorithms and Modulation Schemes](https://ieeexplore.ieee.org/document/8828004)
* Micro-Doppler: [Micro-Doppler Effect in Radar: Phenomenon, Model, and Simulation Study](https://ieeexplore.ieee.org/document/1603402)
* Dynamic Range: [Dynamic Range Considerations for Modern Digital
Array Radars](https://ieeexplore.ieee.org/document/9114607)
* Phase Noise: [On the Safe Road Toward Autonomous Driving: Phase Noise Monitoring in Radar Sensors for Functional Safety Compliance](https://ieeexplore.ieee.org/document/8827996)
* Phase Noise: [Detailed Analysis and Modeling of Phase Noise and Systematic Phase Distortions in FMCW Radar Systems](https://ieeexplore.ieee.org/document/9875949)
* Interference: [Radar Interference Mitigation for Automated Driving: Exploring Proactive Strategies](https://ieeexplore.ieee.org/document/9127843)
* Interference: [Interference in Automotive Radar Systems: Characteristics, Mitigation Techniques, and Current and Future Research](https://ieeexplore.ieee.org/document/8828037)
Automotive Radar Applications:
* Detection,Fusion for Autonomous Driving: [Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges](https://www.mdpi.com/1424-8220/22/11/4208)
* Signal Processing: [Automotive Radar Signal Processing: Research Directions and Practical Challenges](https://ieeexplore.ieee.org/document/9369027)
* Interference: [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://ieeexplore.ieee.org/abstract/document/9802083)
* Semantic Understanding: [Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding](https://link.springer.com/chapter/10.1007/978-3-658-23751-6_1)
* Radar vs Lidar: [Comparative Analysis of Radar and Lidar Technologies for Automotive Applications](https://ieeexplore.ieee.org/document/9760734/)
Other Radar Applications:
* Human Activity Recognition: [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities](https://dl.acm.org/doi/abs/10.1145/3447744)
* Gesture: [Motion Sensing Using Radar: Gesture Interaction and Beyond](https://ieeexplore.ieee.org/abstract/document/8755821)
* Vital Sign: [Contactless Radar-Based Sensors: Recent Advances in Vital-Signs Monitoring of Multiple Subjects](https://ieeexplore.ieee.org/abstract/document/9785580)
* UAV: [Radar Perception for Autonomous Unmanned Aerial Vehicles: a Survey](https://dl.acm.org/doi/10.1145/3522784.3522787)
General Object Detection:
* [3D Object Detection from Images for Autonomous Driving: A Survey](https://arxiv.org/abs/2202.02980)
* [Deep Learning for 3D Point Clouds: A Survey](https://arxiv.org/abs/1912.12033)
* [Attention Mechanisms in Computer Vision: A Survey](https://arxiv.org/abs/2111.07624)
* [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://arxiv.org/abs/2011.10671)
Sensor Fusion:
* Learning: [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://arxiv.org/abs/2106.12735)
* Learning: [Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://ieeexplore.ieee.org/document/9000872)
* Traditional: [Multisensor data fusion: A review of the state-of-the-art](https://www.sciencedirect.com/science/article/abs/pii/S1566253511000558)
* Information: [Information Decomposition of Target Effects from Multi-Source Interactions: Perspectives on Previous, Current and FutureWork](https://www.mdpi.com/1099-4300/20/4/307)
* Uncertainty: [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://link.springer.com/article/10.1007/s10994-021-05946-3)
* Conformal Prediction: [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://arxiv.org/abs/2011.10671)
---
## Recommended Books and Tutorials
### Radar Textbook
* Fundamentals of Radar Signal Processing by Mark A. Richard
* Radar Systems Analysis and Design using Matlab by Bassem R. Mahafza
### Online Course
* [Radar: Introduction to Radar Systems](https://www.ll.mit.edu/outreach/radar-introduction-radar-systems-online-course)
* [Build a Radar](https://llx.mit.edu/courses/course-v1:MITLL+MITLLx01+Q2_2019/about)
* [Radar Systems Engineering](http://radar-course.org/)
* [Adaptive Antennas and Phased Arrays](https://www.ll.mit.edu/outreach/adaptive-antennas-and-phased-arrays-online-course)
### Signal Processing
* [Introduction to mmwaveSensing: FMCW Radars](https://training.ti.com/sites/default/files/docs/mmwaveSensing-FMCW-offlineviewing_0.pdf)
* [The fundamentals of millimeter wave sensors](https://www.ti.com/lit/wp/spyy005a/spyy005a.pdf?ts=1619205965675)
* [Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors](https://ieeexplore.ieee.org/document/9658500)
* [Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers](https://ieeexplore.ieee.org/document/9908570)
### Waveform Comparison
* [Analysis and Comparison of MIMO Radar Waveforms](https://ieeexplore.ieee.org/document/7060251)
### Quadrature Signal
* [Quadrature Signals: Complex, But Not Complicated](https://dspguru.com/dsp/tutorials/quadrature-signals/)
* [Using a complex-baseband architecture in FMCW radar systems](https://www.ti.com/lit/wp/spyy007/spyy007.pdf)
### MIMO
* [TI MIMO radar](https://www.ti.com/lit/an/swra554a/swra554a.pdf)
* [TI EmptyBand DDM (Chinese)]() [(English)]()
---
## Radar Signature
### PointCloud
* 2022-A Data-driven Approach for Stochastic Modeling of Automotive Radar Detections for Extended Objects __`GeMic`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9783497)
* 2020-Performance Evaluation Of Wide Aperture Radar For Automotive Applications __`RadarConf`__; __`0.1Deg`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9266609)
* 2018-Radar and Lidar Target Signatures of Various Object Types and Evaluation of Extended Object Tracking Methods for Autonomous Driving Applications [Paper](https://ieeexplore.ieee.org/abstract/document/8455395)
* 2017-Radar Reflection Characteristics of Vehicles for Contour and Feature Estimation __`FUSION`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8126352)
### RCS
* 2021-Machine Learning-Based Target Classification for MMW Radar in Autonomous Driving [Paper](https://ieeexplore.ieee.org/document/9319548)
* 2021-Performance evaluation of a state-of-the-art automotive radar and corresponding modeling approaches based on a large labeled dataset [Paper](https://www.tandfonline.com/doi/full/10.1080/15472450.2021.1959328)
* 2021-Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms __`RadarConf`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9455239)
* 2018-Review of Radar Classification & RCS Characterisation Techniques for Small UAVs or Drones [Paper](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-rsn.2018.0020)
### Phase
* 2019-Phase-Based Target Classification Using Neural Network in Automotive Radar Systems __`RadarConf`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8835725)
### Motion
* 2021-Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms __`RadarConf`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9455239)
* 2020-New Radar Micro-Doppler Tag for Road Safety Based on the Signature of Rotating Backscatters [Paper](https://ieeexplore.ieee.org/abstract/document/9311635)
* 2019-Motion sensing using radar: Gesture interaction and beyond [Paper](https://ieeexplore.ieee.org/abstract/document/8755821)
### Polarimetric
* 2019-Polarimetric Signatures of a Passenger Car [Paper](https://ieeexplore.ieee.org/abstract/document/8890117)
* 2018-Performance Analysis of 79 GHz Polarimetric Radar Sensors for Autonomous Driving [Paper](https://ieeexplore.ieee.org/abstract/document/8249142)
* 2018-Autonomous Driving Features based on 79 GHz Polarimetric Radar Data [Paper](https://ieeexplore.ieee.org/abstract/document/8546632)
---
## Calibration
### Radar
* 2022-Radar Calibration by Corner Reflectors with Mass-production Errors [Paper](https://ieeexplore.ieee.org/abstract/document/9784534)
* 2022-A novel method for calibration and verification of road side millimetre-wave radar [Paper](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/itr2.12151)
* 2021-Auto-Calibration of Automotive Radars in Operational Mode Using Simultaneous Localisation and Mapping __`TVT`__; [Paper](https://ieeexplore.ieee.org/document/9353252)
* 2020-Motion Based Online Calibration for 4D Imaging Radar in Autonomous Driving Applications __`GeMic`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9080233)
* 2018-Multi-Radar Self-Calibration Method using High-Definition Digital Maps for Autonomous Driving __`ITSC`__; [Paper](https://ieeexplore.ieee.org/document/8569272/)
### Radar-Camera
* 2021-A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration __`ICRA`__; __`Motion`__ ; [Paper](https://ieeexplore.ieee.org/abstract/document/9561938)
* 2021-Spatio-Temporal Multisensor Calibration Based on Gaussian Processes Moving Object Tracking __`ToR`__; __`Trajectory`__ ; [Paper](https://ieeexplore.ieee.org/abstract/document/9387269)
* 2019-Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems __`ITSC`__; __`NN`__; [Paper](https://ieeexplore.ieee.org/document/8917135)
* 2015-Radar and vision sensors calibration for outdoor 3D reconstruction __`ICRA`__; [Paper](https://ieeexplore.ieee.org/document/7139473)
* 2004-Obstacle Detection Using Millimeter-wave Radar and Its Visualization on Image Sequence __`ICPR`__; [Paper](https://ieeexplore.ieee.org/document/1334537/)
### Radar-Lidar
* 2020-Extrinsic and Temporal Calibration of Automotive Radar and 3D LiDAR __`IROS`__; [Paper](https://ieeexplore.ieee.org/document/9341715)
* 2020-Automatic Targetless Extrinsic Calibration of Multiple 3D LiDARs and Radars __`IROS`__; [Paper](https://ieeexplore.ieee.org/document/9340866/)
* 2017-Extrinsic 6DoF calibration of 3D LiDAR and radar __`RCS`__; [Paper](https://ieeexplore.ieee.org/document/8098688)
### Radar-Lidar-Camera
* Continuous Target-free Extrinsic Calibration of a Multi-Sensor System from a Sequence of Static Viewpoints [Paper](https://arxiv.org/abs/2207.03785)
* 2022-OpenCalib: A multi-sensor calibration toolbox for autonomous driving [Paper](https://arxiv.org/abs/2205.14087); [Code](https://github.com/PJLab-ADG/SensorsCalibration)
* 2021-An Joint Extrinsic Calibration Tool for Radar, Camera and Lidar __`TIV`__; [Paper](https://ieeexplore.ieee.org/document/9380784); [Code](https://github.com/tudelft-iv/multi_sensor_calibration)
* 2021-Online multi-sensor calibration based on moving object tracking [Paper](https://www.tandfonline.com/doi/full/10.1080/01691864.2020.1819874)
* 2019-Extrinsic 6DoF Calibration of a Radar – LiDAR– Camera System Enhanced by Radar Cross Section Estimates Evaluation [Paper](https://www.sciencedirect.com/science/article/pii/S0921889018301994)
---
## Labelling
* 2021-Spatio-Temporal Consistency for Semi-supervised Learning Using 3D Radar Cubes __`IV`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9575247)
* 2021-Automatic labeling of vulnerable road users in multi-sensor data __`ITSC`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9564692)
* 2021-Rethinking of Radar’s Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment __`CVPRW`__; __`CRUW`__; [Paper](https://openaccess.thecvf.com/content/CVPR2021W/WAD/html/Wang_Rethinking_of_Radars_Role_A_Camera-Radar_Dataset_and_Systematic_Annotator_CVPRW_2021_paper.html)
* 2021-RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users __`CRV`__; __`RADDet`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9469418)
* 2020-CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations __`ICPR`__; __`CARRADA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9413181)
* 2020-Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets [Paper](https://arxiv.org/abs/2012.01993)
* 2020-Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF) [Paper](https://ml4ad.github.io/files/papers2020/Annotating%20Automotive%20Radar%20efficiently:%20Semantic%20Radar%20Labeling%20Framework%20(SeRaLF).pdf)
* 2020- RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar __`IV`__; __`Oxford`__; __`PoseChain`__; [Paper](https://ieeexplore.ieee.org/document/9304674)
* 2019-Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS __`ICMIM`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8726801)
---
## Data Augmentation
* 2022-Multi-class Road User Detection with 3+1D Radar in the View-of-Delft Dataset __`RAL`__; __`PC`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9699098)
* 2021-Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra __`RadarConf`__; __`RA`__; __`Corruption`__ ; [Paper](https://ieeexplore.ieee.org/document/9455269)
* 2021-Data Augmentation in Time and Doppler Frequency Domain for Radar-based Gesture Recognition __`EuRad`__; __`Spectrogram`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9784553)
* 2020-RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition __`RA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9249018)
* 2020-RADIO Parameterized Generative Radar Data Augmentation for Small Datasets __`RA`__; [Paper](https://www.mdpi.com/2076-3417/10/11/3861)
* 2016-Convolutional Neural Network With Data Augmentation for SAR Target Recognition __`GRSL`__; __`SAR`__; [Paper](https://ieeexplore.ieee.org/abstract/document/7393462)
---
## Simulator
* 2024-RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation __`CVPR`__;
* 2021-MaxRay: A Raytracing-based Integrated Sensing and Communication Framework __`OpenSoucre`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9743510)
* 2021-Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics __`RAL`__; __`OpenSoucre`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9387149); [Code](https://github.com/chstetco/virtualradar)
* 2020-Scalable and Physical Radar Sensor Simulation for Interacting Digital Twins[Paper](https://ieeexplore.ieee.org/abstract/document/9205224)
### Artifacts
* 2020-Simulator Design for Interference Analysis in Complex Automotive Multi-User Traffic Scenarios [Paper](https://ieeexplore.ieee.org/abstract/document/9266318)
* 2019-Modeling and Simulation of Radar Sensor Artifacts for Virtual Testing of Autonomous Driving [Paper](https://mediatum.ub.tum.de/1535151)
### Evaluation
* 2021-Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving __`RadarConf`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9455235)
* 2021-A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving __`IV`__; [Paper](https://ieeexplore.ieee.org/document/9564521)
* 2018-Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving __`ITSC`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8569423)
---
## Generative Model
* 2021-There and Back Again: Learning to Simulate Radar Data for Real-World Applications __`ICRA`__; __`Simulation_to_RA`__; __`Categorical_VAE`__; [Paper](https://arxiv.org/abs/2011.14389)
* 2020-L2R GAN: LiDAR-to-Radar Translation __`ACCV`__; __`Lidar_OGM_to_RD`__; __`Oxford`__ ;__`cGAN`__; [Paper](https://openaccess.thecvf.com/content/ACCV2020/papers/Wang_L2R_GAN_LiDAR-to-Radar_Translation_ACCV_2020_paper.pdf)
* 2020-GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies __`Journal`__; __`RD_to_Image`__; __`Categorical_VAE`__; [Paper](https://arxiv.org/abs/2107.08948)
* 2019-Automotive radar and camera fusion using Generative Adversarial Networks __`Journal`__; __`Radar_OGM_to_Image`__; __`cGAN`__; [Paper](https://www.sciencedirect.com/science/article/abs/pii/S1077314219300530)
* 2017-Deep Stochastic Radar Models __`IV`__; __`Scene_to_RA`__; __`VAE`__; [Paper](https://ieeexplore.ieee.org/document/7995697)
### Doppler
* 2021-IMU2Doppler: Cross-Modal Domain Adaptation for Doppler-based Activity Recognition Using IMU Data __`IMWUT`__; __`IMU`__; [Paper](https://dl.acm.org/doi/abs/10.1145/3494994)
* 2021-Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition __`CHI`__; __`Video`__; [Paper](https://dl.acm.org/doi/abs/10.1145/3411764.3445138)
---
## Testing
* 2021-Millimeter-Wave Radar in-the-Loop Testing for Intelligent Vehicles __`TITS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9507048)
* 2020-On the Testing of Advanced Automotive Radar Sensors by Means of Target Simulators [Paper](https://www.mdpi.com/1424-8220/20/9/2714)
* 2017-Surrogate Bicycle Design for Millimeter-Wave Automotive Radar Pre-Collision Testing __`TITS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/7829378)
---
## MIMO Calibration
* 2021-Auto-Calibration of Automotive Radars in Operational Mode Using Simultaneous Localisation and Mapping __`TVT`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9353252)
* 2021-A Practical Concept for Precise Calibration of MIMO Radar Systems __`EuRAD`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9784495)
---
## Detector
* 2022-A Novel Radar Point Cloud Generation Method for Robot Environment Perception [Paper](https://ieeexplore.ieee.org/abstract/document/9823311)
* 2021-DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar [Paper](https://ieeexplore.ieee.org/abstract/document/9547416)
* 2020-Object surface estimation from radar images [Paper](https://ieeexplore.ieee.org/abstract/document/9054622)
* 2020-Deep temporal detection - A machine learning approach to multiple-dwell target detection [Paper](https://ieeexplore.ieee.org/abstract/document/9114828)
* 2019-DL-CFAR: a Novel CFAR Target Detection Method Based on Deep Learning [Paper](https://ieeexplore.ieee.org/abstract/document/8891420)
* 2019-Feature Detection With a Constant FAR in Sparse 3-D Point Cloud Data [Paper](https://ieeexplore.ieee.org/abstract/document/8924890)
## Super Resolution
* 2024-DART: Implicit Doppler Tomography for Radar Novel View Synthesis __`CVPR`__;[Paper](https://arxiv.org/abs/2403.03896); [Codes](https://github.com/WiseLabCMU/dart)
* 2023-Data-driven Spatial Super-Resolution for FMCW mmWave Sensing Systems [Paper](https://ieeexplore.ieee.org/document/10287476)
* 2023-Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data [Paper](https://arxiv.org/abs/2306.09839)
* 2023-Azimuth Super-Resolution for FMCW Radar in Autonomous Driving __`CVPR`__; [Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Li_Azimuth_Super-Resolution_for_FMCW_Radar_in_Autonomous_Driving_CVPR_2023_paper.html)
* 2023-Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars [Paper](https://ieeexplore.ieee.org/abstract/document/10106481)
* 2022-A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation [Paper](https://ieeexplore.ieee.org/abstract/document/9674901)
* 2020-Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation [Paper](https://ieeexplore.ieee.org/abstract/document/9412858)
## Clustering
* 2021-Supervised Noise Reduction for Clustering on Automotive 4D Radar [Paper](https://ieeexplore.ieee.org/abstract/document/9659953)
* 2019-A Multi-Stage Clustering Framework for Automotive Radar Data [Paper](https://ieeexplore.ieee.org/abstract/document/8916873)
* 2019-Robust and Adaptive Radar Elliptical Density-Based Spatial Clustering and labeling for mmWave Radar Point Cloud Data [Paper](https://ieeexplore.ieee.org/abstract/document/9048869)
* 2018-Supervised Clustering for Radar Applications On the Way to Radar Instance Segmentation [Paper](https://ieeexplore.ieee.org/abstract/document/8443534)
* 2016-Adaptive Clustering for Contour Estimation of Vehicles for High-Resolution Radar [Paper](https://ieeexplore.ieee.org/abstract/document/7533930)
* 2012- Grid-Based DBSCAN for Clustering Extended Objects in Radar Data [Paper](https://ieeexplore.ieee.org/abstract/document/6232167)
## Denoising
* Deep Convolutional Autoencoder Applied for Noise Reduction in Range-Doppler Maps of FMCW Radars
* Learning from Natural Noise to Denoise Micro-Doppler Spectrogram
---
## TI Reference Designs
* TIDEP-01027 High-end corner radar reference design __`AWR2944`__; [Link](https://www.ti.com/tool/TIDEP-01027)
* TIDEP-01025 mmWave diagnostic and monitoring reference design [Link](https://www.ti.com/tool/TIDEP-01025)
* TIDEP-01024 Obstacle detection reference design using 76-Ghz to 81-GHz antenna-on-package (AoP) mmWave sensor __`AWR1843AOP`__; [Link](https://www.ti.com/tool/TIDEP-01024)
* TIDEP-01023 Child-presence and occupant-detection reference design using 60-GHz antenna-on-package mmWave sensor __`AWR6843AOP`__; [Link](https://www.ti.com/tool/TIDEP-01023)
* TIDEP-01021 Beamsteering for corner radar reference design __`AWR1843BOOST`__; [Link](https://www.ti.com/tool/TIDEP-01021)
* TIDEP-01018 Automated doors reference design using TI mmWave sensors __`IWR6843ISK`__; [Link](https://www.ti.com/tool/TIDEP-01018)
* TIDEP-01013 Gesture controlled HMI with mmWave sensors and Sitara™ processors reference design __` IWR6843ISK`__; [Link](https://www.ti.com/tool/TIDEP-01013)
* TIDEP-01012 Imaging radar using cascaded mmWave sensor reference design __`AWR2243`__; [Link](https://www.ti.com/tool/TIDEP-01012)
* TIDEP-01011 Automated parking system reference design using 77-GHz mmWave sensor __`AWR1843`__; [Link](https://www.ti.com/tool/TIDEP-01011)
* TIDEP-01010 Area scanner using mmWave Sensor with integrated antenna-on-package reference design __`IWR6843`__; [Link](https://www.ti.com/tool/TIDEP-01010)
* TIDEP-0104 Obstacle detection reference design using 77-GHz mmWave sensor __`AWR1642`__; [Link](https://www.ti.com/tool/TIDEP-0104)
* TIDEP-01006 Autonomous robot reference design using ROS on Sitara™ MPU & antenna-on-package mmWave sensors __`IWR6843`__; [Link](https://www.ti.com/tool/TIDEP-01006)
* TIDEP-01003 Zone occupancy detection using mmWave sensor reference design __` IWR1443BOOST`__; [Link](https://www.ti.com/tool/TIDEP-01003)
* TIDEP-01001 Vehicle occupant detection reference design __`AWR6843`__; [Link](https://www.ti.com/tool/TIDEP-01001)
* TIDEP-01000 People Counting and Tracking Reference Design Using mmWave Radar Sensor __`IWR6843`__; [Link](https://www.ti.com/tool/TIDEP-01000)
* TIDEP-0094 80m-Range Object Detection Reference Design With Integrated Single-Chip mmWave Sensor __`IWR1642`__; [Link](https://www.ti.com/tool/TIDEP-0094)
* TIDEP-0092 Short-range radar (SRR) reference design __`IWR1642`__; [Link](https://www.ti.com/tool/TIDEP-0092)
* TIDEP-0091 Power optimization for 77GHz-level transmitter reference design __`IWR1443`__; [Link](https://www.ti.com/tool/TIDEP-0091)
* TIDEP-0090 Traffic Monitoring Object Detection and Tracking Reference Design Using mmWave Radar Sensor [Link](https://www.ti.com/tool/TIDEP-0090)
---
### Classification of Clusters
* 2015-[Making Bertha See Even More](https://ieeexplore.ieee.org/abstract/document/7161279)
* 2017-[Comparison of Random Forest and Long Short-Term Memory Network Performances in Classification Tasks Using Radar](https://ieeexplore.ieee.org/document/8126350)
* 2018-[Radar-based Feature Design and Multiclass Classification for Road User Recognition](https://ieeexplore.ieee.org/abstract/document/8500607)
* 2019-[Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles](https://ieeexplore.ieee.org/abstract/document/8813773)
* 2020-[Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification](https://ieeexplore.ieee.org/abstract/document/9190338)
* 2021-[Deep Learning for Automotive Object Classification with Radar Reflections](https://ieeexplore.ieee.org/abstract/document/9455334)
---
## Object Detection
* 2024-Bootstrapping Autonomous Radars with Self-Supervised Learning __`CVPR`__; [Paper](https://arxiv.org/abs/2312.04519)
* 2024-RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features __`CVPR`__;
* 2024-SIRA: Scalable Inter-frame Relation and Association for Radar Perception __`CVPR`__;
* 2023-SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar __`VOD`__; __`TJ4D`__; [Paper](https://arxiv.org/abs/2307.10784)
* 2023-PeakConv: Learning Peak Receptive Field for Radar Semantic Segmentation __`CVPR`__; [Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.html)
* 2023-Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection __`KRadar`__; [Paper](https://arxiv.org/abs/2303.06342)
* Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information __`Oxford`__; __`RADIATE`__; [Paper](https://arxiv.org/abs/2302.10450)
* 2022-A recurrent CNN for online object detection on raw radar frames__`CARRADA`__; __`ROD`__; [Paper](https://arxiv.org/abs/2212.11172)
* 2022-Radatron: Accurate Detection Using Multi-Resolution Cascaded MIMO Radar __`ECCV`__; [Paper](https://link.springer.com/chapter/10.1007/978-3-031-19842-7_10)
* 2022-Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data __`Segmentation`__; __`RadarScenes`__; __`RAL`__; [Paper](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/zeller2022ral.pdf)
* 2022-3D Object Detection for Multi-frame 4D Automotive Millimeter-wave Radar Point Cloud __`3DDetection`__; __`TJ4D`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9944629)
* 2022-mmWave-YOLO: A mmWave Imaging Radar-Based Real-Time Multiclass Object Recognition System for ADAS Applications [Paper](https://ieeexplore.ieee.org/document/9777730)
* 2022-NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving [Paper](https://arxiv.org/abs/2209.14499)
* 2022-ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals __`Segmentation`__;__`CARRADA`__; [Paper](https://arxiv.org/abs/2209.12940)
* 2022-Raw High-Definition Radar for Multi-Task Learning __`CVPR`__; __`RADIAL`__; [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Rebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html)
* 2022-Exploiting Temporal Relations on Radar Perception for Autonomous Driving __`CVPR`__; __`Oxford`__; [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html)
* 2022-Deep Instance Segmentation with Automotive Radar Detection Points __`TIV`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9762032)
* 2022-Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar
* 2022-HARadNet: Anchor-free target detection for radar point clouds using hierarchical attention and multi-task learning
* Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-cloud Data
* 2021-Radar Voxel Fusion for 3D Object Detection [Paper](https://www.mdpi.com/2076-3417/11/12/5598); [Code](https://github.com/TUMFTM/RadarVoxelFusionNet)
* 2021-Quantification of Uncertainties in Deep Learning - based Environment Perception [Paper](https://arxiv.org/pdf/2306.03018.pdf)
* 2021-Graph Convolutional Networks for 3D Object Detection on Radar Data __`ICCVW`__; __`3DDetection`__; [Paper](https://openaccess.thecvf.com/content/ICCV2021W/AVVision/html/Meyer_Graph_Convolutional_Networks_for_3D_Object_Detection_on_Radar_Data_ICCVW_2021_paper.html)
* 2021-High-resolution radar road segmentation using weakly supervised learning __`Segmentation`__; [Paper](https://www.nature.com/articles/s42256-020-00288-6)[Code](https://github.com/itaiorr/radar_road_seg)
* 2021-Multi-View Radar Semantic Segmentation __`ICCVW`__; __`CARRADA`__; __`Segmentation`__; [Paper](https://openaccess.thecvf.com/content/ICCV2021/html/Ouaknine_Multi-View_Radar_Semantic_Segmentation_ICCV_2021_paper.html); [Code](https://github.com/valeoai/MVRSS)
* 2021-RPFA-Net: a 4D RaDAR Pillar Feature Attention Network for 3D Object Detection [Paper](https://ieeexplore.ieee.org/abstract/document/9564754/)
* 2021-Towards Pedestrian Detection in Radar Point Clouds with Pointnets [Paper](https://dl.acm.org/doi/abs/10.1145/3459066.3459067)
* 2021-Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [Paper](https://iopscience.iop.org/article/10.1088/1742-6596/1924/1/012003/pdf)
* 2021-A Neural Network Based System for Efficient Semantic Segmentation of Radar Point Clouds [Paper](https://link.springer.com/article/10.1007/s11063-021-10544-4)
* 2020-Leveraging radar features to improve point clouds segmentation with neural networks [Paper](https://link.springer.com/chapter/10.1007/978-3-030-48791-1_8)
* 2019-[Experiments with mmWave Automotive Radar Test-bed](https://ieeexplore.ieee.org/abstract/document/9048939)
* 2019-[Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors](https://openaccess.thecvf.com/content_ICCVW_2019/html/CVRSUAD/Major_Vehicle_Detection_With_Automotive_Radar_Using_Deep_Learning_on_Range-Azimuth-Doppler_ICCVW_2019_paper.html)
* 2020-[CNN Based Road User Detection Using the 3D Radar Cube](https://ieeexplore.ieee.org/abstract/document/8962258)
* 2020-[RAMP-CNN A Novel Neural Network for Enhanced Automotive Radar Object Recognition](https://ieeexplore.ieee.org/abstract/document/9249018)
* 2019-[2D Car Detection in Radar Data with PointNets](https://ieeexplore.ieee.org/abstract/document/8917000)
* 2020-[Detection and Tracking on Automotive Radar Data with Deep Learning](https://ieeexplore.ieee.org/document/9190261)
* 2020-[Pointillism Accurate 3D Bounding Box Estimation with Multi-Radars](https://dl.acm.org/doi/abs/10.1145/3384419.3430783)
* 2020-[Radar-based 2D Car Detection Using Deep Neural Networks](https://ieeexplore.ieee.org/abstract/document/9294546)
* 2021-[Radar Voxel Fusion for 3D Object Detection](https://www.mdpi.com/2076-3417/11/12/5598)
* 2021-[Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation](https://www.mdpi.com/2076-3417/11/6/2599)
* 2019-[Deep Radar Detector](https://ieeexplore.ieee.org/abstract/document/8835792)
* 2019-[mID Tracking and Identifying People with Millimeter Wave Radar](https://ieeexplore.ieee.org/abstract/document/8804831)
* 2020-[Improved and Optimal DBSCAN for Embedded Applications Using High-Resolution Automotive Radar](https://ieeexplore.ieee.org/abstract/document/9253774)
* 2020-[MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring](https://ieeexplore.ieee.org/abstract/document/9114662)
* 2020-[Through Fog High Resolution Imaging Using Millimeter Wave Radar](https://openaccess.thecvf.com/content_CVPR_2020/html/Guan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html)
* 2020-[Deep Learning on Radar Centric 3D Object Detection](https://arxiv.org/abs/2003.00851)
* 2021-[Radar Transformer An Object Classification Network Based on 4D MMW Imaging Radar](https://www.mdpi.com/1424-8220/21/11/3854)
* 2021-[mmPose-NLP A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars](https://arxiv.org/abs/2107.10327)
### Pre-CFAR Data
#### Range Azimuth Map
* 2019-[Deep Learning-based Object Classification on Automotive Radar Spectra](https://ieeexplore.ieee.org/abstract/document/8835775)
* 2020-[Image Segmentation and Region Classification in Automotive High-Resolution Radar Imagery](https://ieeexplore.ieee.org/abstract/document/9288850)
* 2020-[YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems](https://www.mdpi.com/1424-8220/20/10/2897)
* 2020-[300 GHz radar object recognition based on deep neural networks and transfer learning](https://arxiv.org/abs/1912.03157)
* 2020-[2020-Probabilistic Oriented Object Detection in Automotive Radar](https://openaccess.thecvf.com/content_CVPRW_2020/html/w6/Dong_Probabilistic_Oriented_Object_Detection_in_Automotive_Radar_CVPRW_2020_paper.html)
* 2021-[Perception Through 2D-MIMO FMCW Automotive Radar Under Adverse Weather](https://arxiv.org/abs/2104.01639)
* 2021-[Deep-Learning Based Decentralized Frame-to-Frame Trajectory Prediction Over Binary Range-Angle Maps for Automotive Radars]()
#### Range Doppler Map
* 2018-[Single-Frame Vulnerable Road Users Classification with a 77 GHz FMCW Radar Sensor and a Convolutional Neural Network](https://ieeexplore.ieee.org/abstract/document/8448126)
* 2018-[Moving Target Classification in Automotive Radar Systems Using Convolutional Recurrent Neural Networks](https://ieeexplore.ieee.org/abstract/document/8553185)
* 2019-[A Study on Radar Target Detection Based on Deep Neural Networks](https://ieeexplore.ieee.org/abstract/document/8629967)
* 2020-[Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network](https://ieeexplore.ieee.org/abstract/document/9054511)
* 2021-[Detecting High-Speed Maneuvering Targets by Exploiting Range-Doppler Relationship for LFM Radar](https://ieeexplore.ieee.org/abstract/document/9347707)
* 2021-[DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification](https://ieeexplore.ieee.org/document/9564526)
#### ROD2021 Challenge Paper
* 2021-[Radar Object Detection Using Data Merging, Enhancement and Fusion](https://dl.acm.org/doi/10.1145/3460426.3463653)
* 2021-[Squeeze-and-Excitation network-Based Radar Object Detection with Weighted Location Fusion](https://dl.acm.org/doi/abs/10.1145/3460426.3463654)
* 2021-[Scene-aware Learning Network for Radar Object Detection](https://dl.acm.org/doi/10.1145/3460426.3463655)
* 2021-[DANet: Dimension Apart Network for Radar Object Detection](https://dl.acm.org/doi/10.1145/3460426.3463656)
* 2021-[Efficient-ROD: Efficient Radar Object Detection based on Densely Connected Residual Network](https://dl.acm.org/doi/abs/10.1145/3460426.3463657)
* 2021-[ROD2021 Challenge: A Summary for Radar Object Detection Challenge for Autonomous Driving Applications](https://dl.acm.org/doi/10.1145/3460426.3463658)
---
## Sensor Fusion
* 2024-RCBEVDet: Radar-camera Fusion in Bird’s Eye View for 3D Object Detection __`CVPR`__; [Paper](https://arxiv.org/abs/2305.15883)
* 2024-Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions __`CVPR`__;
* 2024-CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation __`CVPR`__;
* 2023-CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception __`nuScenes`__; [Paper](https://openaccess.thecvf.com/content/ICCV2023/html/Kim_CRN_Camera_Radar_Net_for_Accurate_Robust_Efficient_3D_Perception_ICCV_2023_paper.html)
* 2023-LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion __`VOD`__; __`CVPR`__; [Paper](https://arxiv.org/abs/2307.00724)
* 2023-RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems [Paper](https://arxiv.org/abs/2211.06108)
* 2023-Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection __`CVPR`__; [Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Bi-LRFusion_Bi-Directional_LiDAR-Radar_Fusion_for_3D_Dynamic_Object_Detection_CVPR_2023_paper.html)
* 2023-MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion __`nuScenes`__; [Paper](https://arxiv.org/abs/2302.10511)
* 2022-RADIANT: Radar-Image Association Network for 3D Object Detection __`AAAI`__; __`nuScenes`__; [Paper](http://cvlab.cse.msu.edu/pdfs/Long_Kumar_Morris_Liu_Castro_Chakravarty_AAAI2023.pdf)
* 2022-Detecting darting out pedestrians with occlusion aware sensor fusion of radar and stereo camera ; __`TIV`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9941368)
* 2022-CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection; __`ECCV`__; __`RADIATE`__; [Paper](https://arxiv.org/abs/2210.09267)
* DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars __`nuScenes`__ ; [Paper](https://arxiv.org/abs/2209.12729)
* 2022-CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer __`nuScenes`__ ; [Paper](https://arxiv.org/abs/2209.06535)
* 2022- Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection __`BEVFeature`__; __`nuScenes`__ ; [Paper](https://arxiv.org/abs/2208.12079)
* 2022- RadSegNet: A Reliable Approach to Radar Camera Fusion __`RADIATE`__; [Paper](https://arxiv.org/abs/2208.03849)
* 2022- HRFuser: A Multi-resolution Sensor Fusion
Architecture for 2D Object Detection __`CrossAttention`__; __`nuScenes`__ ; __`DENSE`__ ; [Paper](https://arxiv.org/abs/2206.15157); [Code](https://arxiv.org/abs/2206.15157)
* 2022-A Simple Baseline for BEV Perception Without LiDAR __`BEVFeature`__; __`nuScenes`__ ;[Paper](https://arxiv.org/abs/2206.07959)
* 2022-Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection __`CVPR`__; __`TeacherStudent`__ ;__`Oxford_Foggy`__; [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Li_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html)
* 2022-Global-Local Feature Enhancement Network for Robust Object Detection using mmWave Radar and Camera __`ICASSP`__; __`ROI+Transformer`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9746764)
* 2022-Radar Guided Dynamic Visual Attention for Resource-Efficient RGB Object Detection __`IJCNN`__; __`RadarROI`__; __`nuScenes`__ ; [Paper](https://arxiv.org/abs/2206.01772)
* 2021-RVDet:Feature-level Fusion of Radar and Camera for Object Detection __`ITSC`__; __`BEVFeature`__ ; __`Fisheye_Camera`__; [Paper](https://ieeexplore.ieee.org/document/9564627)
* 2021-Radar Camera Fusion via Representation Learning in Autonomous Driving; __`CVPRW`__; __`VisualSemantics`__; [Paper](https://openaccess.thecvf.com/content/CVPR2021W/MULA/html/Dong_Radar_Camera_Fusion_via_Representation_Learning_in_Autonomous_Driving_CVPRW_2021_paper.html); [Video](https://www.youtube.com/watch?v=kBkdw4qFznU&t=5s)
* 2021-Robust Small Object Detection on the Water Surface through Fusion of Camera and MillimeterWave Radar __`ICCV`__; __`Attention`__; [Paper](https://openaccess.thecvf.com/content/ICCV2021/html/Cheng_Robust_Small_Object_Detection_on_the_Water_Surface_Through_Fusion_ICCV_2021_paper.html)
* 2021-Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals __`CVPR`__; __`Attention`__; __`Oxford_Foggy`__; [Paper](https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html); [Code](https://github.com/qiank10/MVDNet)
* 2021-CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking __`IV`__; __`CenterFusion+Track`__; __`nuScenes`__; [Paper](https://arxiv.org/abs/2107.05150); [Video](https://www.youtube.com/watch?v=_vuO19L6L0Q)
* 2019-[Distant Vehicle Detection Using Radar and Vision](https://ieeexplore.ieee.org/document/8794312)
* 2019-[RVNet: Deep Sensor Fusion of Monocular Camera and Radar for Image-based Obstacle Detection in Challenging Environments](https://link.springer.com/chapter/10.1007/978-3-030-34879-3_27)
* 2019-[Radar and Camera Early Fusion for Vehicle Detection in Advanced Driver Assistance Systems](https://ml4ad.github.io/files/papers/Radar%20and%20Camera%20Early%20Fusion%20for%20Vehicle%20Detection%20in%20Advanced%20Driver%20Assistance%20Systems.pdf)
* 2019-[A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://ieeexplore.ieee.org/document/8916629)
* 2019-[Deep Learning Based 3D Object Detection for Automotive Radar and Camera](https://ieeexplore.ieee.org/document/8904867)
* 2020-[YOdar Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors](https://arxiv.org/abs/2010.03320v2)
* 2020-[Radar+ RGB Fusion For Robust Object Detection In Autonomous Vehicle](https://ieeexplore.ieee.org/abstract/document/9191046)
* 2020-[Low-level Sensor Fusion for 3D Vehicle Detection using Radar Range-Azimuth Heatmap and Monocular Image](https://openaccess.thecvf.com/content/ACCV2020/html/Kim_Low-level_Sensor_Fusion_Network_for_3D_Vehicle_Detection_using_Radar_ACCV_2020_paper.html)
* 2020-[Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor](https://www.mdpi.com/1424-8220/20/4/956)
* 2019-[RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles](https://ieeexplore.ieee.org/document/8803392)
* 2020-[CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://arxiv.org/abs/2011.04841)
* 2020-[Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles](https://arxiv.org/abs/2009.08428)
* 2020-[GRIF Net: Gated Region of Interest Fusion Network for Robust 3D Object Detection from Radar Point Cloud and Monocular Image](https://ieeexplore.ieee.org/document/9341177)
* 2021-[milliEye: A Lightweight mmWave Radar and Camera Fusion System for Robust Object Detection](http://aiot.ie.cuhk.edu.hk/papers/milliEye.pdf)
* 2021-[3D Detection and Tracking for On-road Vehicles with a Monovision Camera and Dual Low-cost 4D mmWave Radars](https://ieeexplore.ieee.org/document/9564904)
* 2011-[Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications](https://www.mdpi.com/1424-8220/11/9/8992)
* 2017-[Comparative Analysis of RADAR- IR Sensor Fusion Methods for Object Detection](https://ieeexplore.ieee.org/abstract/document/8204237?casa_token=Iclafffn7ZEAAAAA:0zfHdvHe2VXd3mQOW_AMjexp-fL4zfzyTZ7CKesXw7jnKEuWe0Ty-akJBW4HYg8pkfJtzfPhz5k)
* 2019-[People Tracking by Cooperative Fusion of RADAR and Camera Sensors](https://ieeexplore.ieee.org/document/8917238)
* 2019-[A DNN-LSTM based Target Tracking Approach using mmWave Radar and Camera Sensor Fusion](https://ieeexplore.ieee.org/abstract/document/9058168)
* 2020-[Autonomous Obstacle Avoidance for UAV based on Fusion of Radar and Monocular Camera](https://ieeexplore.ieee.org/abstract/document/9341432?casa_token=hZJ5haSOVqEAAAAA:UoczK2JQxnrkU_rdXKERsm6oVDtLwtembw1iB-dBrrKuZqfbDjgSA4phgCNRI0H-cxGuj_d8NqY)
* 2019-[Extending Reliability of mmWave Radar Trackingand Detection via Fusion With Camera](https://ieeexplore.ieee.org/abstract/document/8844649)
* 2019-[People Tracking by Cooperative Fusion ofRADAR and Camera Sensors](https://ieeexplore.ieee.org/abstract/document/8917238)
* 2019-[TargetDetection Algorithm Based on MMW Radar and Camera Fusion](https://ieeexplore.ieee.org/abstract/document/8917504)
* 2019-[A DNN-LSTM based Target Tracking Approachusing mmWave Radar and Camera Sensor Fusion](https://ieeexplore.ieee.org/abstract/document/9058168?casa_token=-51KkM4RDJUAAAAA:DiM3jG_heIcHkxgsAmrE5ewfVRSrqbp24ChSzRAKYY-nXboD9KZKOp0G1Jl4B0PKi53UnhBfZCI)
* 2020-[A Roadside Camera-Radar Sensing Fusion System for Intelligent Transportation](https://ieeexplore.ieee.org/abstract/document/9337488?casa_token=4Wws9Vyc4UcAAAAA:ktkw199jc7Wtlk2CjHDxOPMQnPCTWINWTZUqEuuTFuL3TsC-P3_U7wqSEQfOPNj72oNt98KFATY)
* 2020-[Cooperative Multi-Sensor Tracking of VulnerableRoad Users in the Presence of Missing Detections](https://www.mdpi.com/1424-8220/20/17/4817)
* 2020-[Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera ](https://www.mdpi.com/1424-8220/21/4/1116)
* 2017-[Vehicle Tracking Using Extended Object Methods: An Approach for Fusing Radar and Laser](https://ieeexplore.ieee.org/document/7989029)
* 2019-[Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS](https://ieeexplore.ieee.org/abstract/document/8726801)
* 2019-[Learning to see through haze: Radar-based Human Detection for Adverse Weather Conditions](https://ieeexplore.ieee.org/document/8870954)
* 2020-[LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion](https://arxiv.org/abs/2010.00731)
* 2020-[RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects](https://arxiv.org/abs/2007.14366)
* 2016-[Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking](https://ieeexplore.ieee.org/document/7283636)
* 2020-[High Dimensional Frustum PointNet for 3D Object Detection from Camera, LiDAR, and Radar](https://ieeexplore.ieee.org/abstract/document/9304655)
* 2020-[Seeing Through FogWithout Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather](https://ieeexplore.ieee.org/abstract/document/9304655)
* 2021-[Radar Voxel Fusion for 3D Object Detection](https://www.mdpi.com/2076-3417/11/12/5598)
---
## Weakly Supervised
* 2022-A Novel Radar Point Cloud Generation Method for Robot Environment Perception [Paper](https://ieeexplore.ieee.org/abstract/document/9823311)
* 2022-Look, Radiate, and Learn: Self-supervised Localisation via Radio-Visual Correspondence __`Arxiv`__; __`Simulation`__; __`SpatialContrastive`__; [Paper](https://arxiv.org/abs/2206.06424)
* 2021-R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes __`3DIMPVT`__; __`nuScenes`__; __`SSL`__; [Paper](https://arxiv.org/abs/2108.04814)
* 2021- RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization __`IJSTSP`__; __`CRUW`__; __`ConfMap`__; [Paper](https://ieeexplore.ieee.org/document/9353210); [Code](https://github.com/yizhou-wang/RODNet); [Video](https://www.youtube.com/watch?v=UZbxI4o2-7g)
* 2020- RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar __`IV`__; __`Oxford`__; __`PoseChain`__; [Paper](https://ieeexplore.ieee.org/document/9304674)
* 2020-Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications __`TVT`__; __`Velocity`__; [Paper](https://arxiv.org/abs/2012.12809)
* 2020-Weakly Supervised Deep Learning Method for Vulnerable Road User Detection in FMCW Radar __`ITSC`__; __`Tracking`__; [Paper](https://ieeexplore.ieee.org/document/9294399)
* 2020- Radar as a Teacher: Weakly Supervised Vehicle Detection using Radar Labels __`ICRA`__; __`CoTeaching`__; [Paper](https://ieeexplore.ieee.org/document/9196855)
---
## Depth Estimation
* 2023-Depth Estimation From Camera Image and mmWave Radar Point Cloud __`CVPR`__; [Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Singh_Depth_Estimation_From_Camera_Image_and_mmWave_Radar_Point_Cloud_CVPR_2023_paper.html)
* 2022-RVMDE: Radar Validated Monocular Depth Estimation for Robotics __`Arxiv`__; __`nuScenes`__ [Paper](https://arxiv.org/abs/2109.05265); [Code](https://github.com/MI-Hussain/RVMDE)
* 2021-Semantic-guided radar-vision fusion for depth estimation and object detection __`BMVC`__; __`nuScenes`__; __`SemanticJoint`__; [Paper](https://biblio.ugent.be/publication/8713974)
* 2021-Depth estimation from monocular images and sparse radar using deep ordinal regression network __`ICIP`__; __`nuScenes`__; __`DORN`__ ; [Paper](https://ieeexplore.ieee.org/abstract/document/9506550); [Code](https://github.com/lochenchou/DORN)
* 2021-R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes __`3DIMPVT`__; __`nuScenes`__; __`SSL`__; [Paper](https://arxiv.org/abs/2108.04814)
* 2021-Radar-Camera Pixel Depth Association for Depth Completion __`CVPR`__; __`nuScenes`__; __`MER`__ ; [Paper](https://openaccess.thecvf.com/content/CVPR2021/html/Long_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.html); [Code](https://github.com/longyunf/rc-pda)
* 2020-Depth Estimation from Monocular Images and Sparse Radar Data __`IROS`__; __`nuScenes`__; __`TwoStage`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9340998); [Code](https://github.com/brade31919/radar_depth)
* 2020-Camera-Radar Fusion for 3-D Depth Reconstruction __`IV`__; __`RadarRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9304559); [Video](https://www.youtube.com/watch?v=T9c75fmmxyQ)
---
## Ego Motion Estimation
* 2022-A Credible and Robust Approach to Ego-Motion Estimation Using an Automotive Radar __`RAL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9743799)
* 2021-3D ego-Motion Estimation Using low-Cost mmWave Radars via Radar Velocity Factor for Pose-Graph SLAM __`ICRA`__; [Paper](https://ieeexplore.ieee.org/document/9495184)
* 2020-milliEgo: Single-chip mmWave Aided Egomotion Estimation with Deep Sensor Fusion __`SenSys`__; [Paper](https://dl.acm.org/doi/abs/10.1145/3384419.3430776); [Code](https://github.com/ChristopherLu/milliEgo)
* 2020-Radar-Inertial Ego-Velocity Estimation for Visually Degraded Environments __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9196666)
* 2018-Precise Ego-Motion Estimation with Millimeter-Wave Radar under Diverse and Challenging Conditions __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8460687)
* 2014-Instantaneous Ego-Motion Estimation using Multiple Doppler Radars __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/6907064)
---
## Velocity Estimation
* 2023-Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision __`CVPR`__; [Paper](https://arxiv.org/abs/2303.00462)
* 2022-Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks __`nuScenes`__; [Paper](https://arxiv.org/abs/2207.03146)
* 2022-Self-Supervised Scene Flow Estimation with 4D Automotive Radar __`RAL`__; __`4DRadar`__; __`FlowNet`__; [Paper](https://arxiv.org/abs/2203.01137); [Code](https://github.com/Toytiny/RaFlow)
* 2021-Full-Velocity Radar Returns by Radar-Camera Fusion __`ICCV`__; __`nuScenes`__; __`OpticalFlow`__; [Paper](https://openaccess.thecvf.com/content/ICCV2021/html/Long_Full-Velocity_Radar_Returns_by_Radar-Camera_Fusion_ICCV_2021_paper.html)
* 2021-3D Radar Velocity Maps for Uncertain Dynamic Environments __`IROS`__; __`Bayesian`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9636019); [Code](https://github.com/RansML/BDF)
* 2020-An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking __`IROS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9341127)
* 2018- Instantaneous Actual Motion Estimation with a Single High-Resolution Radar Sensor __`Nonlinear`__ [Paper](https://ieeexplore.ieee.org/document/8443553)
* 2014-Instantaneous Full-Motion Estimation of Arbitrary Objects using Dual Doppler Radar __`DualRadar`__; [Paper](https://ieeexplore.ieee.org/document/6856449)
* 2013-Instantaneous lateral velocity estimation of a vehicle using Doppler radar __`MultiPts`__; [Paper](https://ieeexplore.ieee.org/abstract/document/6641086)
---
## Tracking
### Neural Network
* 2022- Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time
[Paper](https://arxiv.org/abs/2306.06126)
* 2022-Exploiting Temporal Relations on Radar Perception for Autonomous Driving __`CVPR`__; __`Oxford`__; __`Attention`__; [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html)
* 2021-End-to-End On-Line Multi-object Tracking on Sparse Point Clouds Using Recurrent Convolutional Networks [Paper](https://link.springer.com/chapter/10.1007/978-3-030-86380-7_33)
* 2021-CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking __`IV`__; __`CenterFusion+Track`__; __`nuScenes`__; [Paper](https://arxiv.org/abs/2107.05150); [Video](https://www.youtube.com/watch?v=_vuO19L6L0Q)
### Bayesian Filtering
* 2021-Road-Map Aided GM-PHD Filter for Multi-Vehicle Tracking with Automotive Radar [Paper](https://ieeexplore.ieee.org/document/9403944)
* 2021-Bayesradar: Bayesian Metric-Kalman Filter Learning for Improved and Reliable Radar Target Classification [Paper](https://ieeexplore.ieee.org/abstract/document/9596290)
* 2021-Extended Object Tracking assisted Adaptive Multi-Hypothesis Clustering for Radar in Autonomous Driving Domain [Paper](https://ieeexplore.ieee.org/abstract/document/9466233)
* 2021-A Graph-Based Track-Before-Detect Algorithm for Automotive Radar Target Detection [Paper](https://ieeexplore.ieee.org/document/9276431)
* 2020-BAAS: Bayesian Tracking and Fusion Assisted Object Annotation of Radar Sensor Data for Artificial Intelligence Application __`RadarConf`__; [Paper](https://ieeexplore.ieee.org/document/9266698)
* 2020-An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking __`IROS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9341127)
* 2021-Automotive Radar-Based Vehicle Tracking Using Data-Region Association __`TITS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9525315)
* 2020-Extended Object Tracking Using Spatially Resolved Micro-Doppler Signatures __`TIV`__; [Paper](https://ieeexplore.ieee.org/document/9247291)
* 2019-Extended Object Tracking assisted Adaptive Clustering for Radar in Autonomous Driving Applications [Paper](https://ieeexplore.ieee.org/abstract/document/8916658)
* 2018- Rfcm for Data Association and Multitarget Tracking Using 3D Radar __`ICASSP`__; [Paper](https://ieeexplore.ieee.org/document/8461917)
* 2018-Classification Assisted Tracking for Autonomous Driving Domain [Paper](https://ieeexplore.ieee.org/document/8547138)
* 2015-Tracking of Extended Objects with High-Resolution Doppler Radar __`TITS`__; [Paper](https://ieeexplore.ieee.org/document/7355362)
* 2010-Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter __`TSP`__; [Paper](https://ieeexplore.ieee.org/document/5677613)
### Modelling
* 2022-A Data-driven Approach for Stochastic Modeling of Automotive Radar Detections for Extended Objects __`GeMic`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9783497)
* 2021-A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving __`IV`__; [Paper](https://ieeexplore.ieee.org/document/9564521)
* 2021-Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar __`JSTSP`__; [Paper](https://ieeexplore.ieee.org/document/9351598)
* 2020-Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar __`ICASSP`__; [Paper](https://ieeexplore.ieee.org/document/9054614)
---
## Prediction
* 2021-Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks [Paper](https://ieeexplore.ieee.org/abstract/document/9520242)
* 2021-Deep-Learning Based Decentralized Frame-to-Frame Trajectory Prediction Over Binary Range-Angle Maps for Automotive Radars __`TVT`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9437958)
* 2020-LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [Paper](https://arxiv.org/abs/2010.00731)
* 2020-FISHING Net: Future Inference of Semantic Heatmaps in Grids [Paper](https://arxiv.org/abs/2006.09917)
---
## Occupancy Grid Map
* 2022-Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities __`RAL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9689995)
* 2020-See Through Smoke: Robust Indoor Mapping with Low-cost
mmWave Radar __`MobiSys`__; [Paper](https://dl.acm.org/doi/abs/10.1145/3386901.3388945)
* 2019-Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation __`ICCV`__; [Paper](https://openaccess.thecvf.com/content_ICCVW_2019/html/CVRSUAD/Sless_Road_Scene_Understanding_by_Occupancy_Grid_Learning_from_Sparse_Radar_ICCVW_2019_paper.html)
* 2019- Probably Unknown: Deep Inverse Sensor Modelling Radar __`ICRA`__; [Paper](https://ieeexplore.ieee.org/document/8793263)
* 2019-Occupancy Grids Generation Using Deep Radar Network for Autonomous Driving __`ITSC`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8916897)
* 2015-Automotive Radar Gridmap Representations [Paper](https://ieeexplore.ieee.org/abstract/document/7117922)
### Point Cloud Map
* 2022-High Resolution Point Clouds from mmWave Radar [Paper](https://arxiv.org/abs/2206.09273)
* 2020-Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images __`IROS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9340856)
---
## Open Space Segmentation
* 2022-Raw High-Definition Radar for Multi-Task Learning __`CVPR`__; __`Dataset`__; [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Rebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html)
* 2022-Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance [Paper](https://arxiv.org/abs/2203.01442)
* 2022-Drivable Region Estimation for Self-Driving Vehicles Using Radar __`TVT`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9740418)
* 2021-PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain [Paper](https://arxiv.org/abs/2103.03387)
* 2020-Deep Open Space Segmentation using Automotive Radar __`Dataset`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9299052)
* 2018-High Resolution Radar-based Occupancy Grid Mapping and Free Space Detection [Paper](https://www.scitepress.org/papers/2018/66673/)
---
## Scene Understanding
* 2020-Semantic Segmentation on 3D Occupancy Grids for Automotive Radar [Paper](https://ieeexplore.ieee.org/abstract/document/9229096)
* 2020-Statistical Image Segmentation and Region Classification Approaches for Automotive Radar __`EuRAD`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9337399)
* 2019-Scene Understanding With Automotive Radar __`TIV`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8911477)
* 2018-Semantic Segmentation on Radar Point Clouds __`FUSION`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8455344)
---
## Place Recognition
* 2024-TransLoc4D: Transformer-based 4D-Radar Place Recognition __`CVPR`__
* 2022-AutoPlace: Robust Place Recognition with Single-chip Automotive Radar __`ICRA`__; [Paper](https://ieeexplore.ieee.org/document/9811869); [Code](https://github.com/ramdrop/AutoPlace)
* 2021-Contrastive Learning for Unsupervised Radar Place Recognition [Paper](https://ieeexplore.ieee.org/abstract/document/9659335)
* 2021-Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning [Paper](https://www.frontiersin.org/articles/10.3389/frobt.2021.661199/full)
* 2021-Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos [Paper](https://arxiv.org/abs/2106.06703)
* 2020-Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance [Paper](https://ieeexplore.ieee.org/abstract/document/9109951)
* 2020-MulRan Multimodal Range Dataset for Urban Place Recognition __`ICRA`__; __`Dataset`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9197298)
---
## Odometry and SLAM
### Odometry
* 2022-Radar Odometry on SE(3) with Constant Acceleration Motion Prior and Polar Measurement Model [Paper](https://arxiv.org/abs/2209.05956)
* 2022-Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry [Paper](https://ieeexplore.ieee.org/document/9812063) __`ICRA`__; [Code](https://github.com/applied-ai-lab/f-mbym)
* 2021-Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning [Paper](https://arxiv.org/abs/2105.14152)
* 2021-Radar Odometry on SE(3) With Constant Velocity Motion Prior __`RAL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9463737)
* 2022-What Goes Around: Leveraging a Constant-curvature Motion Constraint in Radar Odometry __`RAL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9808131)
* 2021-Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation __`RAL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9327473); [Code](https://github.com/keenan-burnett/yeti_radar_odometry)
* 2021-A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9561413)
* 2021-BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation [Paper](https://arxiv.org/abs/2109.09669)
* 2021-CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry __`IROS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9636253)
* 2021-Continuous-time Radar-inertial Odometry for Automotive Radars __`IROS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9636014)
* 2021-Oriented surface points for efficient and accurate radar odometry [Paper](https://arxiv.org/abs/2109.09994)
* 2020-PhaRaO: Direct Radar Odometry using Phase Correlation __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9197231)
* 2019-Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information __`CoRL`__; [Paper](https://proceedings.mlr.press/v100/barnes20a)
### SLAM
* 2022-CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy __`RAS`__; [Paper](https://www.sciencedirect.com/science/article/pii/S0921889022000768)
* 2022-Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization [Paper](http://128.84.4.18/abs/2203.10174)
* 2021-SERALOC: SLAM on semantically annotated radar point-clouds __`ITSC`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9564693)
* 2021-RaLL: End-to-End Radar Localization on Lidar Map Using Differentiable Measurement Model __`TITS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9370010)
* 2021-Improved Radar Localization on Lidar Maps Using Shared Embedding [Paper](https://arxiv.org/abs/2106.10000)
* 2021-Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions __`RAL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9362209)
* 2021-RadarLoc: Learning to Relocalize in FMCW Radar __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9560858)
* 2021-Radar SLAM: A Robust SLAM System for All Weather Conditions [Paper](https://arxiv.org/abs/2104.05347)
* 2020-RadarSLAM: Radar based Large-Scale SLAM in All Weathers __`IROS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9341287)
* 2020-Self-Supervised Localisation between Range Sensors and Overhead Imagery [Paper](https://arxiv.org/abs/2006.02108)
* 2020-RSL-Net: Localising in Satellite Images From a Radar on the Ground __`RAL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8957240)
* 2020-Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9196835)
* 2020-Radar-on-Lidar: metric radar localization on prior lidar maps [Paper](https://ieeexplore.ieee.org/abstract/document/9303291)
* 2020-kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation [Paper](https://www.mdpi.com/1424-8220/20/21/6002)
* 2020-Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning __`ICRA`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9196682)
* 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://ieeexplore.ieee.org/abstract/document/9341419)
* 2005-An Augmented State SLAM formulation for Multiple Line-of-Sight Features with Millimetre Wave RADAR __`IROS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/1545232)
---
## Automotive SAR
* 2022-Synthetic Aperture Radar Imaging of Moving Targets for Automotive Applications __`EuRAD`__; __`Bosch`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9784564)
* 2022-Performance Analysis of Automotive SAR With Radar Based Motion Estimation __`GM`__; [Paper](https://arxiv.org/abs/2204.10406)
* 2022-Residual Motion Compensation in Automotive MIMO SAR Imaging __`RadarConf`__; __`Huawei`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9764310)
* 2022-A Quick and Dirty processor for automotive forward SAR imaging __`RadarConf`__; __`Huawei`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9764234)
* 2021-Cooperative Synthetic Aperture Radar in an Urban Connected Car Scenario __`Huawei`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9376348)
* 2021-Navigation-Aided Automotive SAR for High-Resolution Imaging of Driving Environments __`Huawei`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9363205)
* 2021-Imaging radar for automated driving functions __`Continental`__; [Paper](https://www.cambridge.org/core/journals/international-journal-of-microwave-and-wireless-technologies/article/imaging-radar-for-automated-driving-functions/81B92D1CCF86309E8A354783A343861E)
* 2021-MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving __`TVT`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9465646)
* 2020-3D Point Cloud Generation with Millimeter-Wave Radar [Paper](https://dl.acm.org/doi/abs/10.1145/3432221)
* 2020-High Resolution Radar Dataset for Semi-Supervised Learning of Dynamic
Objects __`CVPRW`__; [Paper](https://openaccess.thecvf.com/content_CVPRW_2020/html/w6/Mostajabi_High-Resolution_Radar_Dataset_for_Semi-Supervised_Learning_of_Dynamic_Objects_CVPRW_2020_paper.html)
### ISAR
* 2022-Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images [Paper](https://ieeexplore.ieee.org/abstract/document/9695280)
* 2021-Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey [Paper](https://ieeexplore.ieee.org/abstract/document/9513303/)
---
## Human Activity Recognition
### Pointcloud
* 2022-Cross Vision-RF Gait Re-identification with Low-cost RGB-D
Cameras and mmWave Radars [Paper](https://www.research.ed.ac.uk/en/publications/cross-vision-rf-gait-re-identification-with-low-cost-rgb-d-camera)
* 2022-Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Sparse Point Clouds __`TMC`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9720163)
* 2021-m-Activity Accurate and Real-Time Human Activity Recognition Via Millimeter Wave Radar __`ICASSP`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9414686)
* 2020-Real-time Arm Gesture Recognition in Smart Home Scenarios via MillimeterWave Sensing [Paper](https://dl.acm.org/doi/abs/10.1145/3432235)
* 2019-RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar [Paper](https://dl.acm.org/doi/abs/10.1145/3349624.3356768)
### Micro-Doppler
* 2022-Attention-Based Dual-Stream Vision Transformer for Radar Gait Recognition __`ICASSP`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9746565)
* 2021-Human Motion Recognition With Limited Radar Micro-Doppler Signatures __`TGRS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9222330)
* 2018-Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks __`TGRS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/8307105)
### RD
* 2021-RadarNet: Efficient Gesture Recognition Technique Utilizing a Miniature Radar Sensor __`CHI`__; [Paper](https://dl.acm.org/doi/abs/10.1145/3411764.3445367)
* 2016-Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum [Paper](https://dl.acm.org/doi/abs/10.1145/2984511.2984565); [Video](https://youtu.be/ZSkl9zoNZRY)
* 2015-Short-Range FMCW Monopulse Radar for Hand-Gesture Sensing __`RadarConf`__; [Paper](https://ieeexplore.ieee.org/abstract/document/7131232)
### Domain Adaption
* 2022-Unsupervised Learning for Human Sensing Using Radio Signals __`WACV`__; [Paper](https://openaccess.thecvf.com/content/WACV2022/html/Li_Unsupervised_Learning_for_Human_Sensing_Using_Radio_Signals_WACV_2022_paper.html)
* 2022-Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy [Paper](https://arxiv.org/abs/2203.04588)
* 2022-mTransSee: Enabling Environment-Independent mmWave Sensing Based Gesture Recognition via Transfer Learning [Paper](https://dl.acm.org/doi/abs/10.1145/3517231)
* 2022-Few-Shot User-Definable Radar-Based Hand Gesture Recognition at the Edge [Paper](https://ieeexplore.ieee.org/abstract/document/9722880)
* 2021-Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal [Paper](https://arxiv.org/abs/2111.06195)
* 2021-Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures __`TGRS`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9467531)
* 2021-Supervised Domain Adaptation for Few-Shot Radar-Based Human Activity Recognition [Paper](https://ieeexplore.ieee.org/abstract/document/9558812)
* 2021-Towards Cross-Environment Human Activity Recognition Based on Radar Without Source Data [Paper](https://ieeexplore.ieee.org/abstract/document/9551721)
* 2021-Continual Learning of Micro-Doppler Signature-Based Human Activity Classification __`GRSL`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9319850)
### Distributed
* 2022-Exploiting Radar Data Domains for Classification with Spatially Distributed Nodes [Paper](https://research.tudelft.nl/en/publications/exploiting-radar-data-domains-for-classification-with-spatially-d)
---
## Radar-Audio
### Speech Recovery
* 2022-Wavesdropper: Through-wallWord Detection of Human Speech via
Commercial mmWave Devices [Paper](https://dl.acm.org/doi/abs/10.1145/3534592)
* 2022-Radio2Speech: High Quality Speech Recovery from Radio Frequency Signals [Paper](https://arxiv.org/abs/2206.11066)
* 2021-mmPhone: Acoustic Eavesdropping on Loudspeakers via mmWave-characterized Piezoelectric Effect __`INFOCOM`__; [Paper](https://ieeexplore.ieee.org/abstract/document/9796806)
### Vocal Chord
* 2022-Multi-target Time-Varying Vocal Folds Vibration
Detection Using MIMO FMCW Radar [Paper](https://ieeexplore.ieee.org/abstract/document/9765794)
* 2021-VocalPrint: A mmWave-based Unmediated Vocal Sensing
System for Secure Authentication [Paper](https://ieeexplore.ieee.org/abstract/document/9444641)
### Separation
* 2022-RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System [Paper](https://arxiv.org/abs/2204.07092)
* 2021-Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals __`SenSys`__; [Paper](https://dl.acm.org/doi/abs/10.1145/3485730.3485945)
---
## Weather Effects
### Effect
* 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://ieeexplore.ieee.org/abstract/document/8666747)
* Seeing through dust and water vapor: Millimeter wave radar sensors for mining applications [Paper](https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.20166)
* Analysis of rain clutter detections in commercial 77 GHz automotive radar [Paper](https://ieeexplore.ieee.org/abstract/document/8249138)
* The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review [Paper](https://www.mdpi.com/1424-8220/20/22/6532)
* Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions [Paper](https://www.mdpi.com/2076-3417/9/11/2341)
* Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques [Paper](https://ieeexplore.ieee.org/document/9307324)
* Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions [Paper](https://www.mdpi.com/2076-3417/9/11/2341)
* What Happens for a ToF LiDAR in Fog? [Paper](https://ieeexplore.ieee.org/abstract/document/9121741)
### Datasets
* Oxford Foggy [Code]((https://github.com/qiank10/MVDNet))
* DENSE
* RADIATE
* Boreas
* K-Radar
See the dataset section for details.
### Methods
* 2022-Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Li_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html)
* 2021-Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals [Paper](https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html)
* 2020-Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather [Paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Bijelic_Seeing_Through_Fog_Without_Seeing_Fog_Deep_Multimodal_Sensor_Fusion_CVPR_2020_paper.html); [Code](); [Video](https://www.youtube.com/watch?v=HPT4nsCkT5Q)
* 2020-Through Fog High Resolution Imaging Using Millimeter Wave Radar [Paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Guan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html); [Code](https://github.com/JaydenG1019/HawkEye-Data-Code); [Video](https://www.youtube.com/watch?v=HPT4nsCkT5Q)
---
## Multi-Path Effect
### Dataset
* 2021-The Radar Ghost Dataset – An Evaluation of Ghost Objects in Automotive Radar Data [Paper](https://ieeexplore.ieee.org/abstract/document/9636338); [Code](https://github.com/flkraus/ghosts)
### Methods
* 2021-Anomaly Detection in Radar Data Using PointNets [Paper](https://ieeexplore.ieee.org/document/9564730)
* 2021-Fast Rule-Based Clutter Detection in Automotive Radar Data [Paper](https://ieeexplore.ieee.org/abstract/document/9564776)
* 2021-Radar Ghost Target Detection via Multimodal Transformers [Paper](https://ieeexplore.ieee.org/document/9497756)
* 2021-Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network [Paper](https://ieeexplore.ieee.org/abstract/document/9413247)
* 2020- Using Machine Learning to Detect Ghost Images in Automotive Radar [Paper](https://ieeexplore.ieee.org/abstract/document/9294631)
* 2020-Seeing Around Street Corners Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar [Paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Scheiner_Seeing_Around_Street_Corners_Non-Line-of-Sight_Detection_and_Tracking_In-the-Wild_Using_CVPR_2020_paper.html)
* 2019-Identification of Ghost Moving Detections in
Automotive Scenarios with Deep Learning [Paper](https://ieeexplore.ieee.org/abstract/document/8726704)
* 2018-Automotive Radar Multipath Propagation in Uncertain Environments [Paper](https://ieeexplore.ieee.org/abstract/document/8570016)
---
## Mutual Interference
### Methods
* 2022-A Two-stage DNN Model with Mask-gated Convolution for Automotive Radar Interference Detection and Mitigation [Paper](https://ieeexplore.ieee.org/document/9770068)
* 2021-Resource-Efficient Deep Neural Networks for Automotive Radar Interference Mitigation [Paper](https://ieeexplore.ieee.org/document/9364355)
* 2021-A DNN Autoencoder for Automotive Radar Interference Mitigation [Paper](https://ieeexplore.ieee.org/document/9413619)
* 2021-CFAR-Based Interference Mitigation for FMCW Automotive Radar Systems [Paper](https://ieeexplore.ieee.org/abstract/document/9541302)
* 2021-Mutual Interference Suppression Using Wavelet Denoising in Automotive FMCW Radar Systems [Paper](https://ieeexplore.ieee.org/document/8946905)
* 2020-Interference Characterization in FMCW radars [Paper](https://ieeexplore.ieee.org/abstract/document/9266283)
* 2020-Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals [Paper](https://ieeexplore.ieee.org/abstract/document/9114627); [Video](https://www.youtube.com/watch?v=DJY9Zk1p9-g)
* 2019-Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks [Paper](https://ieeexplore.ieee.org/document/9011164)
* 2018-A Deep Learning Approach for Automotive Radar Interference Mitigation [Paper](https://ieeexplore.ieee.org/document/8690848)
### Reviews
* 2022-Interference Suppression Using Deep Learning: Current Approaches and Open Challenges [Paper](https://ieeexplore.ieee.org/abstract/document/9802083)
* 2020-Radar Interference Mitigation for Automated Driving: Exploring proactive strategies [Paper](https://ieeexplore.ieee.org/abstract/document/9127843)
* 2020-Interference in Automotive Radar Systems Characteristics, mitigation techniques, and current and future research [Paper](https://ieeexplore.ieee.org/document/8828037)
## Range and Doppler Cell Migration
* 2021-Doppler–Range Processing for Enhanced High-Speed Moving Target Detection Using LFMCW Automotive Radar [Paper](https://ieeexplore.ieee.org/abstract/document/9506841)
* 2019-Range and Doppler Cell Migration in Wideband Automotive Radar [Paper](https://ieeexplore.ieee.org/abstract/document/8695853)
## Tx-Rx Leakage
* 2022-Mitigation of Leakage and Stationary Clutters in Short-Range FMCW Radar With Hybrid Analog and Digital Compensation Technique [Paper](https://ieeexplore.ieee.org/abstract/document/9583883)
* 2019-The Effect of Antenna Mutual Coupling on MIMO Radar System Performance [Paper](https://ieeexplore.ieee.org/abstract/document/8581509)
## Imperfect Waveform Separation
* 2020-Slow-Time MIMO-FMCW Automotive Radar Detection with Imperfect Waveform Separation [Paper](https://ieeexplore.ieee.org/document/9053892)