Event Location

College of Engineering CANVAS Seminar
Monday, March 19, 2018
9:00 a.m. - 10:00 a.m. 1502/3 EB

Make Self-Driving Cars See! The Key MIMO Radar Technology for Autonomous

Dr. Shunqiao Sun, Aptiv (Delphi), Technical Center Malibu


Radar not only has found widespread application in advanced driver assistance systems (ADAS) but also is one of the key technologies to enable environmental perception for autonomous driving under all kinds of weather conditions. Today, a single self-driving car has been equipped with more than 10 radars, enabling a radar-based 360 degree surround sensing. The radar sensors with high resolution and multi-functionality are highly demanded for autonomous driving. As compared to traditional
phased-array radar system with the same number of transmit and receive antennas, multiple-input multiple-output (MIMO) radar achieves significantly improved spatial resolution by exploiting waveform diversity. Due to its advantages, MIMO radar technology has been widely used in designing millimeter-wave radar sensors for ADAS and self-driving cars.

The Part I of the talk will introduce the topic with a review on radar system architecture for autonomous driving and the fundamentals of MIMO radar. The presentation will introduce novel MIMO radar approaches with the emerging sparse sensing techniques, including compressive sensing (CS) and matrix completion (MC). The proposed MIMO radar approach with sparse
sensing enables high target scene surveillance while requiring substantially reduced volume of data as compared to state-of-art radar systems. The Part II of the talk will focus on automotive radar signal processing for autonomous driving. The topics will include radar-based 360 degree environmental perception, high resolution target angle estimation with a networked MIMO radar system, and radar interference mitigation. Future work including imaging radar, “smart” radar sensors with machine learning and deep learning for autonomous driving applications, such as pedestrian detection will be discussed.


Dr. Shunqiao Sun received his Ph.D. degree in Electrical and Computer Engineering from Rutgers, The State University of New Jersey in Jan., 2016. He is currently with the radar core team of Aptiv (Delphi), Technical Center Malibu, CA where he is working on advanced radar signal processing and machine learning algorithms for self-driving cars. In the past, he held internships at Cisco Systems, Shanghai, China and Mitsubishi Electric Research Labs (MERL), Cambridge, MA. His research interests lie at the interface of statistical and sparse signal processing with mathematical optimizations, MIMO radar, machine learning, and smart sensing for autonomous vehicles. Dr. Sun has been awarded the 2015-2016 Rutgers University ECE Graduate Program Academic Achievement Award. He is also the winner of 2016 IEEE Aerospace and Electronic Systems Society Robert T. Hill Best Dissertation Award for his thesis “MIMO Radars with Sparse Sensing”.

Faculty host: Dr. Linos J Jacovides