Bayesian compressive sensing based SAR imaging for GMTI system

With the increase of the imaging resolution, the resulting enormous amount of sampling raw data aggravates transmission and storage load for multi-channel synthetic aperture radar (SAR) system. Considering the fact that the correlation among the dual-channel SAR images is high, we propose a Bayesian compressive sensing (BCS) based SAR imaging algorithm for ground moving targets indication (GMTI) system, which uses Laplace priors on the basis coefficients in a hierarchical manner. The simulation results show that the proposed algorithm can successfully detect the moving target and meanwhile suppress the static clutter scattering centers, with 50% sampling data of those required by the range-Doppler (RD) algorithm.