SAR Imaging of Moving Targets by Subaperture based Low-rank and Sparse Decomposition

We propose a subaperture based method for synthetic aperture radar (SAR) imaging of moving targets. It exploits low-rank and sparse decomposition for extraction of moving targets from the complex SAR scene. First SAR raw data are divided into subapertures in the azimuth direction. Subsequently, low-rank and sparse decomposition is applied using the multiple subapertures data to accomplish the separation of moving targets from the stationary SAR background. A full resolution moving target image is reconstructed by combining the spectral information of the sparse subaperture images. Such an image has a high signal to clutter ratio and is well suited for motion estimation and focusing algorithms. This proposed framework extends the applicability of sparsity-driven moving target focusing methods to very low signal to clutter ratio environments. We demonstrate the performance of our approach through experiments with synthetic and real SAR data.