An Iterative Adaptive Reweighted Minimization Sparsity Autofocus Algorithm via Bayesian Recovery for Array SAR Imaging

The influence of phase error in echo signal is rarely considered or corrected by most classical compressed sensing (CS) algorithms, and reduces the quality of imaging results. In order to improve the quality of array synthetic aperture radar (ASAR) imaging, a new CS algorithm called Iterative Adaptive Reweighted Norm Minimization Sparsity Autofocus algorithm via Bayesian Recovery (IARNSABR) was proposed in this paper. Based on the principle of Bayesian Recovery, the iterative adaptive reweighted norm minimization method has been used in the process of reconstruction. The theoretical model and the process of imaging of IARNSABR has been established. And the proposed algorithm can correct the influence of phase error more effectively, and has stronger ability of eliminating the false targets. Through simulation and experiment results, IARNSABR can achieve higher quality imaging than SAFBRIM.