基于联合聚焦/超分辨贝叶斯模型的雷达目标超分辨重建

摘要

针对合成孔径雷达(synthetic aperture radar,SAR)、逆合成孔径雷达(inverse SAR,ISAR)等雷达目标图像,提出了一种基于联合聚焦/超分辨贝叶斯模型的超分辨重建方法.该方法基于联合聚焦/超分辨和点扩散函数参数模型,采用Metropolis-Hastings迭代更新算法,产生一系列描述目标散射截面和散焦参数概率分布特征的样本,从而估计出最佳目标散射截面元和散焦参数,实现低分辨率图像的超分辨重建.以合成与实测图像数据为例,对该超分辨方法进行了演示并给出了重建结果.实验表明,本文提出的方法对雷达目标图像重建效果良好,可用于SAR、ISAR及实波束成像等雷达图像目标信息的开发.%A radar target image super-resolution reconstruction method based on the joint focus/super-resolution Bayesian model is presented. Based on the joint focus/super-resolution and point spread function model and using Metropolis-Hastings algorithm, the method produces a series of samples describing the probability distribution characteristic of the target scattering cross sections and defocusing parameters, thus estimates the optimum defocusing parameter and cross section elements and finally realizes the super-resolution of low-resolution images. The method is illustrated on synthetic and measured images, and the super-resolution representations of low-resolution images are given. The experimental results indicate that the method has a good performance in the radar image reconstruction and may be applied to exploit target information from the radar images produced by synthetic aperture radar (SAR) , inverse synthetic aperture radar (ISAR) or real beam imaging radar.