A sparse optimization method for masking effect removal in noise synthetic aperture radar

In this paper, we propose a sparse optimization method to suppress the masking effect of noise synthetic aperture radar (SAR) imaging. The transmitted noise waveforms have large fluctuations in the spectrum, which results in high-level sidelobes in the range direction. Weak targets can be masked by the sidelobes of strong targets when traditional matched filtering (MF) is applied. Inspired by the idea of compressed sensing (CS), we formulate the noise SAR imaging process as solving an inverse problem from incomplete random frequency measurements, and then reconstruct the noise SAR image using sparse optimization method. Experimental results on real airborne noise SAR data are presented to show the effectiveness of the proposed method.