Enhanced ISAR Imaging and Motion Estimation With Parametric and Dynamic Sparse Bayesian Learning

This paper is focused on high-resolution inverse synthetic aperture radar (ISAR) imaging and motion estimation of maneuvering targets from compressively sampled echo data. Herein, a local structural sparse Bayesian learning (LS-SBL) algorithm is proposed by exploiting the joint sparsity pattern of adjacent scatterers. A structured prior by modeling the neighboring correlation or dependence is utilized to encode the joint sparsity pattern. Meanwhile, a parametric dictionary with unknown rotational parameters is constructed to represent the target maneuverability. The solution to the LS-SBL algorithm is decomposed into iterations between sparse imaging and dictionary learning. In sparse imaging, an expectation-maximization method is employed for ISAR image formation and hyperparameter estimation by using a predesigned dictionary. In dictionary learning, an efficient approach of rotational parameter estimation is presented to dynamically update the parametric dictionary. Due to the exploitation of joint sparsity pattern, enhanced performance of ISAR image reconstruction can be achieved by effectively preserving the target structure. In addition, the cross-range scaled ISAR image is obtainable by extracting the target geometry, which benefits from the rotational motion estimation. Finally, experiments on simulated and measured data demonstrate the effectiveness of the proposed algorithm.