OSRanP: A Novel Way for Radar Imaging Utilizing Joint Sparsity and Low-Rankness

Abstract

Synthetic aperture radar (SAR) has extensive applications in both civilian and military fields for its ability to create high-resolution images of the ground target without being affected by weather conditions and daytime or nighttime effect. As fueled by the several decades’ advancement of SAR, existing SAR systems exhibit high imaging capabilities, namely, significantly high two-dimensional resolution. However, in accordance with Nyquist's sampling theory, the increase in resolution implies an evident increase in the amount of sampling data, thereby causing numerous limitations to practical application. To solve this problem, several compressive sensing (CS) and matrix sensing (MS) techniques have been applied to SAR imaging, wherein the existing knowledge of sparsity or low-rankness is exploited to reconstruct the SAR image based on an under-sampled SAR raw data. In this study, we take a different approach, wherein redundancy property of the SAR image is further exploited. The SAR image is split into a sparse matrix and a low rank matrix. Thus, the SAR imaging processor is modelled as a problem of joint sparse and low rank matrices recovery. An Orthogonal Sparse and Rank-one Pursuit (OSRanP) algorithm is newly proposed to solve this problem in SAR imaging case, where there isn’t any prior information of the exact sparsity or low-rankness value at hand. As revealed from the results here, the proposed method outmatches the CS and MS methods in sampling efficiency in both simulations and experiments.