A MAP Approach for 1-Bit Compressive Sensing in Synthetic Aperture Radar Imaging

Abstract

In this letter, we propose a compressive sensing approach for synthetic aperture radar (SAR) imaging of sparse scenes with 1-bit-quantized data. Within the framework of maximum a posteriori estimation, we formulate the SAR image reconstruction problem as a sparse optimization problem and then solve it using a first-order primal-dual algorithm. The processing results of both simulated and real radar data show that our approach can eliminate the ghost target caused by 1-bit quantization in high signal-to-noise ratio situations and suppress the noisy background very well.