基于贝叶斯检验模型的压缩感知算法及应用

针对正交匹配追踪(OMP)算法需设置冗余的支撑集,导致信号重构时运算量变大、抗噪性能和重构性能变差等问题,提出了一种基于贝叶斯模型的OMP(BOMP,bayesian orthogonal matching pursuit)算法。首先利用贝叶斯检验模型和OMP算法合理去除支撑集中的冗余部分,得到相等或略大于信号真实稀疏度的支撑集;其次构建BOMP的信号重构算法;最后将算法应用于ISAR成像。仿真和实测数据结果表明,由于本文算法可近似估计到信号的真实稀疏度,因此具有更好的抗噪性能以及重构精度,相应的运算量也明显减少。

A signal recovery algorithm for compressed sensing based on Bayesian model and its application

The orthogonal matching pursuit (OMP) algorithm must set a redundant su pport in advance,which leads to heavier computation burden,lower noise suppressi ve ability and poor signal reconstructed p erformance.This paper proposes a Bayesian orthogonal matching pursuit(BOMP) algorithm.Firstly,the redundant support is properly del eted by using Bayesian model and OMP algorithm to obtain a proper support,which is equivalent to the real support or so. Secondly,the BOMP sparse signal reconstruction algo rithm is constructed.Finally,the BOMP algorithm is used for ISAR imaging.Simul ation results and experimental results of real data show that by means of approximate estimation of sparsity,the proposed algorithm can enhance the noise suppressive ability,recon struction accuracy and decrease the computing complexity obviously.