An algorithm for inverse synthetic aperture imaging lidar based on sparse signal representation

Abstract

In actual applications of inverse synthetic aperture imaging lidar, the issue of sparse aperture data arises when continuous measurements are impossible or the collected data during some periods are not valid. Hence, the imaging results obtained by traditional methods are limited by high sidelobes. Considering the sparse structure of actual target space in high frequency radar application, a novel imaging method based on sparse signal representation is proposed in this paper. Firstly, the range image is acquired by traditional pulse compression of the optical heterodyne process. Then, the redundant dictionary is constructed through the sparse azimuth sampling positions and the signal form after the range compression. Finally, the imaging results are obtained by solving an ill-posed problem based on sparse regularization. Simulation results confirm the effectiveness of the proposed method.