On compressed sensing applied to 2-D SAR imaging

Modern Synthetic Aperture Radar (SAR) systems operate with high bandwidths to achieve high resolution. But huge amount of data caused by high resolution and large swath can easily exceed the storage capacity and the transmittal bandwidth. Compressive Sensing (CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of measurements. Under the framework of Compressive Sensing, sampling rate is not related to the effective bandwidth of signal, but determined by the content and structure of the interested information. Combining CS with SAR can settle the restraint relationship between the data quantity and resolution. Even better, for CS algorithm is a signal estimation method, CS-SAR may solve some problems which are brought by match filter method, such as sidelobe suppression, azimuth ambiguity elimination and super-resolution imaging.