Multi-Channel Synthetic Aperture Radar Imaging of Ground Moving Targets Using Compressive Sensing

Integrated with the array technique, multi-channel processing can be applied to synthetic aperture radar of ground moving target imaging (SAR GMTIm), which is very powerful in remote sensing of smart city. To reduce the data sampling amounts, compressive sensing can be used by exploiting a sparse prior of moving targets. In this paper, the SAR GMTIm from data of compressive sampling is addressed by proposing a novel reweighted sparse algorithm. Here, we mainly focus on sparse imaging and clutter suppression for heterogeneous scene of urban areas. In the scheme, the phase of interferogram and the magnitude after displaced phase center antenna are incorporated to derive the weights on sparsity-constraint. Due to the joint usage of magnitude and phase, the proposed reweighted sparse algorithm can improve the performance of clutter suppression. Finally, experiments using the simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.