基于压缩感知的矩阵型联合SAR成像与自聚焦算法

模型准确情况下，压缩感知在合成孔径雷达成像中得到良好应用；但在实际情况中，模型会存在一定误差，这些误差造成图像偏离真实位置、引起散焦降低成像质量.本文提出一种矩阵型联合CS-SAR成像与自聚焦算法，该算法在CS-SAR成像重构方法方面，基于光滑l₀范数方法提出了矩阵型正则化光滑l₀范数重构方法，该方法具有较强容错能力并能直接重构矩阵型信号，能克服现有联合CS-SAR成像与自聚焦算法在计算效率方面的缺陷.最后，通过仿真验证了所提算法的有效性.

Joint Matrix Form SAR Imaging and Autofocus Based on Compressed Sensing

Compressed sensing (CS) has been successfully applied to the synthetic aperture radar (SAR) imaging.These CS-based SAR imaging algorithms generally assume that the model of the imaging system is accurate.However,in practice it is common to encounter model errors which usually introduce unknown phase errors into the acquired data.The phase errors may cause range migration or defocusing.In this paper,an approach for matrix form joint CS-SAR imaging and autofocus is proposed.Based on smoothed (l)0 norm (SL0) algorithm,we develop a matrix form regularized SL0 (MRSL0) algorithm to efficiently perform CS-SAR imaging.The MRSL0 adopts inequality constrain to tolerate phase errors and has fast computation speed due to its matrix form.Experiment results demonstrate that the proposed approach can efficiently reconstruct high quality images using limited amount of measurements.