Statistical Modeling of ISAR Imaging based on Bayesian Compressive Sensing of Pareto Distribution F amily

The paper aims to compare the potential of two popular flexible laws in Pareto distribution family, the Exponentiated generalized Pareto distribution (exGPD) and the Gamma-ParetoIV distribution (GPIVD), for the statistical modeling of inverse synthetic aperture radar (ISAR) imaging data. From the perspective of enhancing compressibility and sparsity of the target echo, a novel model named GPIVD-priorbased Bayesian compressed sensing (GPIVCS) is deducted, the analysis proves that the sparsity and compressibility of the GPIVCS model are obtained, further improve the image quality. And from the perspective of reducing imaging complexity, a Bayesian compressed sensing method based on exGPD prior is proposed (EX-GPCS), compared with the conventional methods, the EX-GPCS method is capable of imaging targets in a short period of time. This paper gives a detailed formula derivation of the imaging process of the above two models. Finally, the stateof-the-art performance of these proposed methods is verified by experiments with a wide range of ISAR images.