A Target-Oriented Bayesian Compressive Sensing Imaging Method With Region-Adaptive Extractor for mmW Automotive Radar

Millimeter-wave (mmW) automotive radar imaging technology has shown significant potential in autopilot assistance systems. The automotive radar with limited aperture can achieve high-resolution images by synthetic aperture technology. However, conventional imaging methods result in strong background clutter and high sidelobe interferences. To solve these problems, we propose a target-oriented Bayesian compressive sensing imaging method with region-adaptive extractor (TO-BCS-RAE) for mmW automotive radar imaging. First, to extract the potential target regions (PTRs) as well as subtract the background clutters outside the PTR in a high-resolution initial image (by synthetic aperture), a region-adaptive extractor (RAE) is developed by utilizing 2-D constant false alarm (CFAR), isolated-point removing, and imaging clustering. Meanwhile, a more accurate prior distribution of target scattering points can be obtained in the PTR. Then, to suppress the background clutters while enhancing the smooth structure of targets in the PTR, a target-oriented Bayesian compressive sensing (TO-BCS) imaging method is proposed by combining the prior probability distributions and inherent continuity of the target scattering points. It can also effectively reduce the high sidelobes. Finally, to verify the effectiveness of TO-BCS-RAE, we conduct experiments on real data collected from an automotive radar with a vehicle platform in three typical driving scenarios. Both simulated and experimental results show the imaging quality of the proposed imaging method over conventional backprojection (BP), orthogonal matching pursuit (OMP), and iterative shrinkage-thresholding algorithm (ISTA) methods.