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Compressed sensing radar amid noise and clutter using interference covariance information

机译：利用干扰协方差信息在噪声和杂波中压缩感知雷达

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摘要

Adaptive radar processing has been shown to be useful in downward-looking radars that must detect moving targets in the midst of strong clutter returns. Compressed sensing has found applications in radar problems but has not been comprehensively studied with respect to clutter and other structured interference. The performance of compressed sensing radar techniques in the presence of clutter is explored herein and compared to existing adaptive radar processing methods, including Space-Time Adaptive Processing (STAP), via Monte Carlo exploration of target detection performance. Finally, we propose extensions to standard ??1 optimization techniques to account for known interference covariance matrix statistics. These extensions outperform current compressed sensing techniques, outperform the fully sampled, nonadaptive matched filter estimate, and approach the performance level of the fully sampled STAP estimate. However, similar detection performance can be achieved at lower computational cost by applying a linear filter using the same covariance information.
机译：自适应雷达处理已被证明在向下看的雷达中很有用，该雷达必须在强杂波返回中检测移动目标。压缩感测已经在雷达问题中找到了应用，但尚未对杂波和其他结构性干扰进行全面研究。本文探讨了在杂波情况下压缩感测雷达技术的性能，并通过对目标检测性能的蒙特卡洛探索，将其与包括空时自适应处理（STAP）在内的现有自适应雷达处理方法进行了比较。最后，我们提出对标准1优化技术的扩展，以解决已知的干扰协方差矩阵统计信息。这些扩展性能优于当前的压缩传感技术，性能优于完全采样的非自适应匹配滤波器估计，并接近完全采样的STAP估计的性能水平。但是，通过使用使用相同协方差信息的线性滤波器，可以以较低的计算成本获得相似的检测性能。

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