基于块稀疏贝叶斯模型的ISAR成像方法

传统ISAR稀疏成像主要针对独立散射点散射系数的重构问题，然而实际情况下目标散射点之间并不是独立存在的，而是以区域或块的形式存在，在该情形下利用常用的稀疏重构算法并不能完全地刻画块状目标的真实结构，因此该文考虑采用块稀疏重构算法进行目标散射系数重建。基于块稀疏贝叶斯模型和变分推理的重构方法(VBGS)，包含了稀疏贝叶斯学习(SBL)方法中参数学习的优点，其利用分层的先验分布来表征未知信号的稀疏块状信息，因而相对于现有的恢复算法能够更好地重建块稀疏信号。该方法基于变分贝叶斯推理原理，根据观测量能自动地估计信号未知参数，而无需人工参数设置。针对稀疏块状目标，该文结合压缩感知(CS)理论将VBGS方法用于ISAR成像，仿真实验成像结果表明该方法优于传统的成像结果，适合于具有块状结构的ISAR目标成像。

ISAR Imaging Method Based on the Bayesian Group-sparse Modeling

The traditional sparse ISAR imaging method mainly considers the recovery of coefficients on individual scatters. However, in the practice situation, the target scatters presented by blocks or groups do not emerge on individual. In this case, the usual sparse recover algorithm can not depict the shape of real target, thus, the group-sparse recover approaches are adopted to reconstruct the coefficients of target scatters. The recovery method based on the Bayesian Group-Sparse modeling and Variational inference (VBGS) uses a hierarchical construction of a general signal prior to model the group sparse signals and contain the merit of Sparse Bayesian Learning (SBL) on parameters learning, as a result, it can reconstruct the group sparse signal better than the usual recover algorithm. The VBGS method uses the variational Bayesian inference approach to estimate the parameters of the unknown signal automatically and does not require the parameter-tuning procedures. Considering the sparse group target, this paper combines the Compress Sensing (CS) theory and the VBGS method to reconstruct the ISAR image. The result of experiments show that the proposed method can improve the imaging accuracy compared with traditional algorithm, and can fit to reconstruct the image of ISAR target which has group structure.