Sparse reconstruction for SAR imaging based on compressed sensing

Abstract

Synthetic Aperture Radar (SAR) can obtain a two-dimensional image of the observed scene. However, the resolution of conventional SAR imaging algorithm based on Matched Filter (MF) theory is limited by the transmitted signal bandwidth and the antenna length. Compressed sensing (CS) is a new approach of sparse signals recovered beyond the Nyquist sampling constraints. In this paper, a high resolution imaging method is presented for SAR sparse targets reconstruction based on CS theory. It shows that the image of sparse targets can be reconstructed by solving a convex optimization problem based on L1 norm minimization with only a small number of SAR echo samples. This indicates the sample size of SAR echo can be considerably reduced by CS method. Super-resolution property and point-localization ability are demonstrated using simulated data. Numerical results show the presented CS method outperforms the conventional SAR algorithm based on MF even though small sample size of SAR echo is used in this method.