基于块剪枝多路径匹配追踪的多信号联合重构  

Joint multi-signal reconstruction based on block pruning multipath matching pursuit

作　　者：司菁菁[1] 候肖兰 程银波[1] 

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004

出　　处：《系统工程与电子技术》2016年第9期1993-1999,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(61471313;61303128);河北省自然科学基金(F2014203183);河北省高等学校科学技术研究项目(Q2012087);燕山大学青年教师自主研究计划(13LGB015)资助课题

摘　　要：针对多路径匹配追踪(multipath matching pursuit,MMP)无法利用稀疏信号的结构信息、迭代层数较高时计算复杂度较大等问题,提出了一种适用于重构块稀疏信号的块剪枝多路径匹配追踪算法。该算法以原子块作为路径扩张的节点,在一定迭代层数后引入剪枝操作,极大地降低了数据运算量。进而,针对多观测向量(multiple measurement vector,MMV)问题,提出了MMV块剪枝MMP算法,用以实现无线传感网小范围内多传感器信号的联合重构。实验表明,块剪枝MMP的重构性能优于MMP,MMV块剪枝MMP的联合重构性能优于MMV块A*正交匹配追踪、MMV子空间匹配追踪和MMV正交匹配追踪。Considering the disadvantages of ignoring sign als structured sparsity and the high complexity inhigh iterative layers in multipath matching pursuit （MMP ）, the block pruning multipath matching pursuit（BPMMP） is proposed to reconstruct the block-sparse signal. In this algorithm, an atomic block serves as anode in the path expansion, and branch pruning operation is introduced after a certain number of iterations.Thus, BPMMP reduces the data processing cost greatly. Moreover, for multiple measurement vector （MMV）problem, BPMMP for MMV （BPMMPMMV） is proposed. It can achieve joint signal reconstruction for multiplesensors within a small range in the wireless sensor network. Experimental results show that BPMMP outperformsMMP on the reconstruction performance, and BPMMPMMV achieves higher joint reconstruction performancethan block A＊ orthogonal matching pursuit for MMV, subspace matching pursuit for MMV and orthogonalmatching pursuit for MMV.

关 键 词：分布式压缩感知 多观测向量 块稀疏 多路径匹配追踪 

分 类 号：TN911.7[电子电信—通信与信息系统]