Block Compressive Sensing in Synthetic Aperture Radar (SAR)

High rate A/D's, heavy computations, large on board memories and downlink throughput are serious problems of high resolution Synthetic Aperture Radar (SAR). Compressive Sensing (CS) theory states that the sparsity or compressibility of a signal makes it possible to reconstruct the signal from a reduced number of samples. In the current paper, the Block Compressive sensing (BCS) is applied to SAR. This work proposes a new framework for SAR imaging in which a reconstruction method called BCS algorithm is used to achieve an excellent resolution with a few number of samples. The suggested technique can reconstruct images well by using nearly 1/3 samples in comparison with other CS image reconstruction methods.