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

Marc Reinecke;Theresa Noegel;Oliver Sura;Marcel Hoffmann;Peter Gulden;Martin Vossiek
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Abstract

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.
汽车前视合成孔径雷达反射目标的抑制
汽车前视合成孔径雷达(FL-SAR)近年来备受关注,不仅因为其分辨率增益,还因为其出色的信杂波比(scr)。然而,当使用反投影(BP)算法进行FL-SAR时,镜像目标问题出现了,这是由于使用二维空间采样网格(例如FL-SAR中创建的采样网格)进行图像重建的固有缺陷。模糊子孔径的构造叠加产生的星等可以显著高于真实目标的星等。这会导致错误的检测,并严重影响更高级别的任务,如轨迹规划。本文旨在利用BP算法的一个众所周知的例子来描述镜像目标现象。在深入了解不良伪影的基础上,开发了四种抑制假检测的方法。通过模拟试验确保其生存能力。在实际测量场景中的实验评估证明了所有方法的有效性和鲁棒性。基于相位相干性的分类方法通过检测图像中的镜像目标特异性特征,产生了最准确的结果,从而增强了FL-SAR的成像能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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