Research Publications
Discover our latest research contributions to the scientific community. Our publications span journal articles, conference papers, and technical reports in radar technology and related fields.
Gait Analysis Using mmWave Radar: A Skeleton-Based Approach with Point Cloud Transformer
Jiahao Tang, Shuting Hu, Siyang Cao, Jennifer Barton, Melvin G. Hector, Mindy J. Fain, Nima Toosizadeh
This study presents a skeleton-based approach for gait analysis using millimeter-wave radar sensors. mmWave radar is non-intrusive, privacy-preserving, unaffected by lighting conditions, and both cost-effective and energy-efficient. Current radar-based gait analysis methods typically use micro-Doppler signatures …
Position Encoding in Detection-Based LiDAR-Camera Matching: A Diagnostic Study At Infrastructure Sites
Lihao Guo, Jiahao Tang, Tam Bang, Tianya Zhang, Austin Harris, Mina Sartipi, Siyang Cao
We present a diagnostic study of learnable position encoding (PE) in the matching front-end of detection-based light detection and ranging (LiDAR)-camera calibration (detect, match, perspective-n-point). Across three infrastructure sites (A9 Highway, UTC3, and UTC4), four PE …
OpenCDA-MARL: A Unified Benchmarking Framework for Cooperative Autonomous Intersection Management With Multi-Agent Reinforcement Learning
L. Guo, L. Liu, J. Tang, B. Liu, S. Cao
Single-vehicle autonomy remains constrained by perception errors and uncoordinated maneuvers, resulting in avoidable collisions and throughput losses at intersections. Cooperative Driving Automation (CDA) offers substantial gains, yet fragmented toolchains hinder progress: industry develops closed systems, while …
CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement
L. Cheng, L. Guo, T. Zhang, T. Bang, A. Harris, M. Hajij, M. Sartipi, S. Cao
CalibRefine is a fully automatic, targetless, and online calibration framework that directly processes raw LiDAR point clouds and camera images. The approach features a Common Feature Discriminator, coarse homography-based calibration, iterative refinement, and attention-based refinement using …
TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection
L. Cheng, S. Cao
TransRAD is a novel 3D radar object detection model that leverages the Retentive Vision Transformer (RMT) to learn features from radar Range-Azimuth-Doppler (RAD) data. The approach incorporates the Retentive Manhattan Self-Attention (MaSA) mechanism to align with …
mmWave Radar for Sit-to-Stand Analysis: A Comparative Study with Wearables and Kinect
S. Hu, P. Ackun, X. Zhang, S. Cao, J. Barton, M. G. Hector, M. J. Fain, N. Toosizadeh
This study presents a comparative analysis of mmWave radar for sit-to-stand movement assessment against established methods including wearable sensors and Kinect systems. The research demonstrates the potential of mmWave radar as a non-contact, privacy-preserving alternative for …
Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors
L. Cheng, A. Sengupta, S. Cao
A deep learning-based method integrating radar and camera data for multi-object tracking. Employs Bi-directional LSTM with tri-output mechanisms for robustness to noise and occlusion, combined with appearance feature model inspired by FaceNet for consistent tracking across …
Radar-Based Fall Detection: A Survey
S. Hu, S. Cao, N. Toosizadeh, J. Barton, M. G. Hector, M. J. Fain
A comprehensive survey of radar-based fall detection systems covering sensor technologies, signal processing techniques, machine learning approaches, and real-world deployment considerations. Emphasizes the effectiveness of Micro-Doppler, Range-Doppler, and Range-Doppler-Angle techniques in ensuring privacy and detecting falls …
mmPose-FK: A Forward Kinematics Approach to Dynamic Skeletal Pose Estimation Using mmWave Radars
S. Hu, S. Cao, N. Toosizadeh, J. Barton, M. G. Hector, M. J. Fain
This paper presents mmPose-FK, a novel approach for human pose estimation using millimeter-wave (mmWave) radar combined with dynamic forward kinematics constraints. The method addresses challenges of low resolution, specularity, and noise artifacts common to mmWave radars …
Radar Range-Doppler Flow: A Radar Signal Processing Technique to Enhance Radar Target Classification
Q. Wen, S. Cao
This work presents Radar Range-Doppler Flow, a novel signal processing technique that enhances radar target classification capabilities. The method improves target discrimination and classification accuracy through advanced processing of range-Doppler data.