Clutter suppression and GMTI with sparse sampled data for dual-channel SAR

With the increase of the swath width and imaging resolution, the resulting enormous amount of sampling raw data aggravates storage and transmission load for Multi-channel synthetic aperture radar (SAR) system. Considering the fact that the correlation among the multi-channel SAR images is high, we propose a compressive sensing (CS)-based ground moving target indication framework with sparse sampled raw data. In the proposed framework, the SAR imaging of one channel is utilized as prior-knowledge, the clutter of other channels is suppressed with only small amount of raw data. Thus the moving targets can be accurately recovered by compressive sensing after clutter suppression. Experiment results demonstrate the proposed method performs well with a very limited number of samples, even if clutter scattering centers are non-sparse.