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 l₀ 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.