基于GPD的稳健贝叶斯压缩感知ISAR成像方法

现有贝叶斯压缩感知（Bayesian Compressed Sensing,BCS）-逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)成像算法中先验分布模型不能很好地满足可压缩性，导致成像精度随脉冲数目的减小、高斯噪声的增强而急剧下降。为此，提出了一种基于广义Pareto分布改进BCS成像方法(Improving BCS imaging based on GPD,IGPCS)。该方法主要在BCS框架下利用广义Pareto先验分布替代传统的广义Gaussian先验分布,以增强模拟信号的稀疏先验和可压缩性。进一步地，为了克服后验概率模型计算困难等问题，采用最大后验（Maximum A Posteriori，MAP）方法对超参数进行估计。通过对Mig-25小型飞机的ISAR模拟实验表明,与传统方法相比，IGPCS方法能够获取极高的成像精度，并且对低脉冲数、强高斯噪声环境具有较强的鲁棒性。

Robust Bayesian Compressed Sensing ISAR Imaging Based on Generalized Pareto Distribution

The prior distribution model in the existing Bayesian compressed sensing inverse synthetic aperture radar(BCS-ISAR) imaging algorithm cannot well meet the compressibility,as a result,the imaging accuracy decreases sharply with the reduction of pulse number and the enhancement of Gaussian noise.In this paper,an improved BCS imaging method based on generalized Pareto distribution(GPD) called IGPCS is proposed.This method mainly uses GPD to replace the general Gaussian distribution(GGD) in the framework of BCS to enhance sparse prior and compressibility.Furthermore,the maximum a posteriori(MAP) method is used to estimate the hyper parameters and overcome the difficulties in calculating the posterior probability model.The ISAR simulation experiment of Mig-25 small aircraft shows that IGPCS method can obtain extremely high imaging accuracy compared with traditional method,and it has strong robustness to low pulse number as well as strong Gaussian noise environment.