Parametric Sparse Representation and Its Applications to Radar Sensing

Abstract

Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally spare representation of the actual radar signals. This paper will introduce a method called parametric sparse representation, which is a special case of dictionary learning and can dynamically learn the unknown factors during the radar sensing and achieve the optimally sparse representation of radar signals. This paper will also introduce the applications of parametric sparse representation to Inverse Synthetic Aperture Radar imaging (ISAR) imaging, Synthetic Aperture Radar imaging (SAR) autofocusing and target recognition based on micro-Doppler effect.