摘要： 为实现稳健逆合成孔径雷达(ISAR)成像,提出基于参数迭代最小化贝叶斯稀疏信号重构的ISAR成像算法。建立ISAR稀疏成像信号模型,通过推导目标参数稀疏贝叶斯模型的联合概率密度函数,将ISAR成像转化为贝叶斯准则下的稀疏约束最大后验概率估计。对目标散射系数和噪声功率交替迭代优化求解,从而实现目标重构。实验结果表明,与SL0算法、OMP算法和BP算法相比,该算法的参数能够自适应调整,具有更好的成像效果。

关键词: 逆合成孔径雷达, 迭代最小化, 稀疏信号重构, 成像, 最大后验概率

Abstract: In order to obtain the robustness Inverse Synthetic Aperture Radar(ISAR) image,an iterative minimization Bayesian learning sparse signal recovery algorithm is proposed.Firstly,ISAR imaging is established,and the imaging problem is converted to sparse constraint Maximum A Posteriori (MAP) estimation under Bayesian criterion.Then,the target reconstruction and the noise power can be obtained simultaneously by exchanging iterative solution,and the target can be recovered.Compared with the conventional sparse recovery algorithms,the proposed algorithm can adjust parameters automatically.Real data experimental results show that the paramters of the proposed algorithm can be adjusted autonomically and have better imaging effect comapred with SL0 algorithm,OMP algorithm and BP algorithm.

Key words: Inverse Synthetic Aperture Radar(ISAR), iterative minimization, sparse signal recovery, imaging, Maximum A Posteriori(MAP)

中图分类号: 

• TP301.6