| Paper ID | SS-12.6 |
| Paper Title |
Four-Dimensional High-Resolution Automotive Radar Imaging Exploiting Joint Sparse-Frequency and Sparse-Array Design |
| Authors |
Shunqiao Sun, University of Alabama, United States; Yimin Zhang, Temple University, United States |
| Session | SS-12: Recent Advances in mmWave Radar Sensing for Autonomous Vehicles |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Recent Advances in mmWave Radar Sensing for Autonomous Vehicles |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We propose a novel automotive radar imaging technique to provide high-resolution information in four dimensions, i.e., range, Doppler, azimuth, and elevation, by exploiting a joint sparsity design in frequency spectrum and array configurations. Random sparse step-frequency waveform is proposed to synthesize a large effective bandwidth and achieve high range resolution profiles. This concept is extended to multi-input multi-output (MIMO) radar by applying phase codes along the slow time to synthesize a two-dimensional (2D) sparse array with a high number of virtual array elements which enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA), and the corresponding URA response is recovered by completing a low-rank block Hankel matrix. The proposed imaging radar provides point clouds with a resolution comparable to LiDAR but with a much lower cost and is insensitive to weather conditions. |
