Soft Iterative Method with Adaptive Thresholding for Reconstruction of Radar Scenes

D. Kozlov, P. Ott, O. Loffeld, Marco Altmann
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Abstract

A proper use of the advances of compressed sensing (CS) theory in radar systems may lead to data rate reduction, energy saving and measuring improvement. Using, e.g., randomly placed chirps and CS reconstruction algorithms, the velocity unambiguously measured by FMCW-Radars can be increased. In this paper, we propose to use the Soft Iterative Method with Adaptive Thresholding (soft-IMAT) for the reconstruction of the radar scene. Its performance is analyzed in different scenarios and compared with the original IMAT with respect to detection probability and computational complexity. A modified soft-IMAT is proposed in order to reduce the computational overhead caused by the soft-based decision.
基于自适应阈值的雷达场景重建软迭代方法
在雷达系统中合理利用压缩感知(CS)理论,可以降低数据速率,节约能源,提高测量精度。例如,使用随机放置的啁啾和CS重建算法,可以提高fmcw -雷达明确测量的速度。本文提出了一种基于自适应阈值法(Soft - imat)的雷达场景重建方法。分析了其在不同场景下的性能,并与原始IMAT在检测概率和计算复杂度方面进行了比较。为了减少软决策带来的计算开销,提出了一种改进的软imat算法。
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