ISAR Imaging Based on Block Bayesian Compressed Sensing by Learning the Clustering Structure

In conventional inverse synthetic aperture radar (ISAR) imaging methods based on compressed sensing (CS), some of the weak scatter points cannot be recovered completely with a short coherent processing interval (CPI). Therefore in this paper, according to the continuity pattern of the ISAR image and location of the dominant scatter points in some clusters, we present a high-resolution ISAR imaging method based on Block Bayesian Compressed Sensing to reconstruction of unknown Sparse Clustering Structure, which is named as BBCS-SCS. For this reason, we first use complex Gaussian prior distribution to model statistical dependencies between neighboring dominant scatterers. Then, a variational Bayesian (VB) inference has been designed and developed to learn the clustering structure of scatterers in the target scene. Simulation results show that the proposed algorithm has better performance in terms of image contrast, image entropy, and running time in comparison with other methods in strong noise.