Parametric Sparse SAR Imaging of Ground Moving Targets Integrated With Motion Compensation

Abstract

Using the array technique, the multi-channel synthetic aperture radar (SAR) has the capability of high-resolution imaging, detection, and location of the ground moving targets, which is a powerful tool during the remote sensing of smart city. Recently, the theory of compressive sensing has been applied to radar imaging with data of compressive sampling, which can effectively reduce the burden of current radar system. In this paper, we focus on SAR moving targets imaging from sparse aperture (SA) data with accurate target motion compensation. The procedure of motion compensation is decomposed into two steps: the SPECAN processing in the range frequency and azimuth time domain is first applied and then followed by the residue component correction embedded into sparse imaging. To overcome the SA and target motion, a novel parametric sparse imaging approach is proposed by addressing the problems of Doppler ambiguity and phase errors. In the scheme, a parametric and dynamic dictionary is used to include these two important issues. Then, a modified orthogonal matching pursuit method is presented for high-quality imaging with Doppler ambiguity number estimation and phase error correction, which can deal with the case of multiple moving targets. Finally, experimental analysis is performed to confirm the effectiveness of the proposed algorithm.