Agenda

Signal Processing Seminar

• Wednesday, 20 September 2023
• 10:30-11:30
• HB 13.140

Automotive Radar for Autonomous Driving: Signal Processing Meets Deep Learning

Millimeter-wave automotive radar emerges as one of key sensing modalities for autonomous driving, providing high resolution in four dimensions (4D), i.e., range, Doppler, and azimuth and elevation angles, yet remain a low cost for feasible mass production. In this talk, we will address the challenges in automotive radar for autonomous driving, examine how signal processing and deep learning can be combined to optimize the performance of automotive radar systems, and outline future research directions. Our focus will be on the generation of high-resolution radar imaging using multi-input multi-output (MIMO) radar and frequency-modulated continuous-wave (FMCW) technology. We will examine the challenges of waveform orthogonality, mutual interference, and sparse antenna array design and present our recent innovations in the field, including sparse array interpolation via forward-backward Hankel matrix completion, fast direction-of-arrival estimation via unrolling iterative adaptive approach, and adaptive beamforming via deep reinforcement learning, leading to the generation of high-resolution low-level automotive radar imaging, represented in bird's-eye view (BEV) format, providing rich shape information for object detection and recognition with deep neural networks. However, the radar BEVs are in general hardly shift-invariant over both angle and range since not every pixel is generated equally. The talk will highlight the importance of physics-aware machine learning in perception task on high-resolution radar imaging. We will show how incorporating radar domain knowledge and signal structure into deep neural network design can lead to more accurate and reliable object detection and recognition. Finally, we will discuss future research directions, including integrated sensing and communication, and collaborative radar imaging via an automotive radar network.

Bio:

Shunqiao Sun received the Ph.D. degree in Electrical and Computer Engineering from Rutgers, The State University of New Jersey under supervision of Prof. Athina Petropulu in Jan. 2016. He is currently an assistant professor at The University of Alabama, Tuscaloosa, AL, USA. From 2016-2019, he was with the radar core team of Aptiv, Technical Center Malibu, California, where he has worked on advanced radar signal processing and machine learning algorithms for self-driving vehicles and lead the development of DOA estimation techniques for next-generation short-range radar sensor which has been used in over 120-million automotive radar units. His research interests lie at the interface of statistical and sparse signal processing with mathematical optimizations, automotive radar, MIMO radar, machine learning, and smart sensing for autonomous vehicles. Dr. Sun has been awarded 2016 IEEE Aerospace and Electronic Systems Society Robert T. Hill Best Dissertation Award for his thesis “MIMO radar with Sparse Sensing”. He authored a paper that won the Best Student Paper Award at 2020 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). He is Vice Chair of IEEE Signal Processing Society Autonomous Systems Initiative (ASI) (2023-2024). He is an associate editor of IEEE Signal Processing Letters and IEEE Open Journal of Signal Processing. He is a Senior Member of IEEE.

Overview of Signal Processing Seminar