High-Resolution Radar Imaging using Enhanced Sparse Bayesian learning

The synthetic aperture radar (SAR) image of moving targets usually has the sparse feature, which provides the sparse approach to improve the imaging performance. In this paper, we address the problem of SAR imaging and motion estimation of maritime targets using parametric and structured sparse Bayesian learning (SBL) approach. To model the motion of maritime targets, a parametric dictionary is used to represent the maneuverability. Meanwhile, a local-structure sparse Bayesian learning (LS-SBL) algorithm is presented by exploiting the structure of the targets. Benefiting from the use of local-structure sparse feature, the imaging performance can be effectively improved with preserving the target structure. Finally, the experimental analysis is performed to confirm the effectiveness of the proposed algorithm.