In recent years, the sparse signal processing technique has shown significant potential in synthetic aperture radar (SAR) imaging, such as image performance improvement and downsampled data-based image recovery. However, due to the huge computational complexity needed, the existing sparse SAR imaging methods, such as conventional observation matrix-based and azimuth-range decouple-based algorithms, are not able to achieve real-time processing, especially for the large-scale scenes, which seriously restricts its application in some fields, e.g., real-time monitoring and early warning. To solve this problem, this article presents a novel real-time sparse SAR imaging method, which can get a similar image performance to that obtained by the existing sparse imaging methods, to reduce the computational complexity to the same order as that required by matched filtering (MF)-based algorithms. This means that with the proposed method, real-time data processing for practical large-scale scene sparse reconstruction becomes possible. Experimental results based on simulated and real data along with a performance analysis are presented to validate the proposed real-time sparse imaging method.

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从理论到应用：实时稀疏 SAR 成像

近年来，稀疏信号处理技术在合成孔径雷达 (SAR) 成像中显示出巨大的潜力，例如图像性能改进和基于下采样数据的图像恢复。然而，现有的稀疏SAR成像方法，如传统的基于观测矩阵和方位距解耦的算法，由于计算量巨大，无法实现实时处理，尤其是对于大尺度场景。 ，严重制约了其在实时监测预警等领域的应用。针对这一问题，本文提出了一种新颖的实时稀疏SAR成像方法，该方法可以获得与现有稀疏成像方法相似的图像性能，将计算复杂度降低到与基于匹配过滤 (MF) 的算法所需的相同数量级。这意味着，通过所提出的方法，实际大规模场景稀疏重建的实时数据处理成为可能。提出了基于模拟和真实数据以及性能分析的实验结果，以验证所提出的实时稀疏成像方法。