Mitigation of Mirror Targets in Automotive Forward-Looking Synthetic Aperture Radar

Automotive forward-looking synthetic aperture radar (FL-SAR) has recently attracted research attention, not only for the resolution gain but also for the exceptional signal-to-clutter ratios (SCRs) that can be achieved. However, when utilizing the backprojection (BP) algorithm for FL-SAR, a mirror-target problem emerges, which is attributable to an inherent flaw of image reconstruction with 2-D spatial sampling grids, such as the ones created in FL-SAR. Constructive superposition of ambiguous subapertures produces magnitudes, which can be significantly higher than those of real targets. This causes false detections and severely impacts higher level tasks such as trajectory planning. This article aims to describe the phenomenon of mirror targets using the well-known example of the BP algorithm. Based on a thorough understanding of the undesirable artifacts, four suppression methods to mitigate false detections were developed. Their viability was ensured through simulative tests. Experimental evaluation in real-world measurement scenarios proved the effectiveness and robustness of all methods. A phase coherency-based classification approach yielded the most accurate results by detecting mirror-target-specific features in the images, thereby enhancing FL-SAR’s imaging capabilities.