Autofocusing ISAR images via sparse representation

While a continuously distributed reflectivity of a scene to be imaged with synthetic aperture radar does not really show sparsity - resulting in a limited performance of compressed sensing for general scenes - the signals of selected objects, characterized by a few prominent scattering centers, and surrounded by a low reflecting environment, have the potential to be represented in a lower-dimensional signal subspace. Especially, for inverse SAR (ISAR) imaging of moving vehicles like cars, ships, flying aircrafts, or even satellites, sparse representation techniques are applicable increasing the performance and image quality. The main problem for this application is the need for the knowledge of the object's motion for a precise motion compensation. Since the information on the motion of the targets normally is only coarse, the radar data themselves have to be exploited to improve motion compensation, i.e. autofocus techniques have to be designed and applied, taylored to the framework of sparse representation and compressed sensing (CS).