Four-Dimensional High-Resolution Automotive Radar Imaging Exploiting Joint Sparse-Frequency and Sparse-Array Design

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 stepfrequency 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 lowrank block Hankel matrix. The proposed imaging radar provides point clouds with a resolution comparable to light detection and ranging (LiDAR) but with a much lower cost and is insensitive to weather conditions.

System Performance of a 79 GHz High-Resolution 4D Imaging MIMO Radar With 1728 Virtual Channels

“…By distributing the antennas in two dimensions, DoA estimation in both azimuth and elevation is enabled, yielding a 4D imaging system [6], [7]. This information depth is required for the development of autonomous driving and future advanced driver assistance systems (ADAS) in the automotive 77 / 79 GHz band [8]- [10]. However, especially in 4D imaging, a large number of channels is required to obtain an accurate DoA estimation with low sidelobes.…”

“…In all these systems, a computationally intensive postprocessing is required to resolve ambiguities, which is errorprone under poor conditions such as low signal-to-noise ratio (SNR) or in complex multi-target cases. For unambiguous, yet very sparse arrays as in [22], a complex reconstruction as shown in [8] is required to reduce the sidelobe level (SLL) in the DoA estimation, having the same limitations as above. To reduce the sparsity and thus effort in the post-processing, a higher channel count is required.…”