LAUSR

This article focuses on capturing and preserving unique structures in inverse synthetic aperture radar (ISAR) imaging procedure, such as structures with little quantity around the edges of targets, to further improve the performance of compressive sensing based ISAR imaging. Specifically, the newly proposed method utilizes the framework of extended block sparse Bayesian learning, and divides the two-dimension ISAR image into overlapping patches for capturing structures. Moreover, the method exploits Pitman-Yor process for learning and clustering space-variant local structures of the overlapping patches adaptively for preserving unique structures, since the power-law property of Pitman-Yor process helps to capture more unique variables in clustering procedure. Meanwhile, this article utilizes variational Bayesian inference for approximating the posterior of the hidden variables and estimating the parameters of the proposed model. Experiment results based on synthetic data, Electromagnetic simulated data, and real measured data, demonstrate the performance of the newly proposed method in obtaining ISAR images with high resolution using fewer measurements. And two metrics, correlation value and structural similarity, illuminate the performance of the method in preserving unique structures and weak scatterers in inherent structures in ISAR imaging.