Redefining Radar Perception for Autonomous Driving: The Role of Sparse Array and Waveform Design in 4D Automotive Radar
Dr. Shunqiao Sun, Dr. Yimin D. Zhang
IEEE Members: $11.00
Non-members: $15.00Length: 06:46
Millimeter-wave automotive radar emerges as one of the key sensing modalities for autonomous driving, providing high-resolution sensing 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 sparse signal processing can be utilized 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, radar mutual interference, and sparse antenna array design, and present our recent innovations in the field, including sparse step-frequency waveform, two-dimensional (2D) sparse arrays to enable drive-over and drive-under functionalities, and techniques to mitigate the high sidelobe of sparse array spectra via forward-backward Hankel matrix completion and sparse array design exploiting multi-frequency signals. The talk will highlight the importance of sparse signal processing in achieving high-resolution radar imaging with low mutual interference at low cost. Finally, we will discuss future research directions, including integrated sensing and communication.