改进的压缩采样匹配追踪算法  

Modified compressed sampling pursuit matching algorithm

作　　者：邢岩辉 林云[1] 

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《计算机应用》2015年第A01期331-334,共4页journal of Computer Applications

基　　金：长江学者和创新团队发展计划项目(IRT1299);重庆市科委重点实验室专项经费资助项目(CSTC)

摘　　要：针对压缩采样匹配追踪(Co Sa MP)算法重构精度相对较差的问题,为了提高算法的重构性能,提出了一种基于伪逆处理改进的压缩采样匹配追踪(MCo Sa MP)算法。首先,在迭代前,对观测矩阵进行伪逆处理,以此来降低原子间的相干性,从而提高原子选择的准确性;然后,结合正交匹配追踪算法(OMP),将OMP算法迭代K次后的原子和残差作为Co Sa MP算法的输入;最后,每次迭代后,通过判断残差是否小于预设阈值来决定算法是否终止。实验结果表明,无论是对一维高斯随机信号还是二维图像信号,MCo Sa MP算法的重构效果优于Co Sa MP算法,能够在观测值相对较少的情况下,实现信号的精确重构。Aiming at the problem that Compressed Sampling Matching Pursuit （ CoSaMP） algorithm has low accuracy in reconstruction, in order to improve the reconstruction performance of CoSaMP algorithm, , based on pseudo-inverse processing, an improved greedy algorithm—Modified Compressed Sampling Matching Pursuit （ MCoSaMP ） was proposed. Firstly, before each iteration, the proposed algorithm did pseudo-inverse processing on observation matrix, which could reduce the coherence between the atoms, thereby improving the accuracy of the selected atoms. Secondly, combined with Orthogonal Matching Pursuit （ OMP） algorithm, MCoSaMP used the atoms and residual as the input parameters of CoSaMP after OMP algorithm iterating K times. Finally, after each iteration, the residual was used to determine whether to stop algorithm by being under a preset threshold or not. The experimental results show that the proposed algorithm performs better than CoSaMP algorithm for both one-dimensional Gaussian random signal and two-dimensional image signal, which can exactly reconstruct the original signal with relatively small number of observations.

关 键 词：压缩感知 稀疏信号 贪婪算法 压缩采样匹配追踪 正交匹配追踪 

分 类 号：TN911.7[电子电信—通信与信息系统]