Sub-Nyquist SAR Imaging Based on Pseudo-Random Space-Time Modulation under Different Compressive Sensing Algorithms

A novel sub-Nyquist SAR based on pseudo-random space-time modulation has been proposed to increase swath width for the sparse scene while preserving the azimuthal resolution. Comparing to the traditional high-resolution wide-swath (HRWS) system, e.g., the azimuthal multi-channels SAR and multi-input multi-output (MIMO) SAR, with large antenna and amount of data, it applies single-channel and overcomes the limitation of Nyquist theorem based on compressive sensing (CS) theorem. CS algorithms are important to sub-Nyquist SAR imaging, and include three algorithms, i.e., greedy algorithm,
$\mathcal{L}_{1}$
-norm optimization algorithm and Bayesian-based method. This paper presents the comparative work of sub-Nyquist SAR imaging based on pseudo-random space-time modulation under different CS algorithms by simulation of real SAR images.