Maximal-minimal correlation atoms algorithm for sparse recovery  

作　　者：Wei Gan Luping Xu Hua Zhang 

机构地区：[1]School of Electronic Engineering, Xidian University

出　　处：《Journal of Systems Engineering and Electronics》2013年第4期579-585,共7页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (61172138);the Specialized Research Fund for the Doctoral Program of Higher Education (200807011007);the Natural Science Basic Research Program in Shannxi Province of China (2013JQ8040)

摘　　要：A new iterative algorithm is proposed to reconstruct an unknown sparse signal from a set of projected measurements. Unlike existing greedy pursuit methods which only consider the atoms having the highest correlation with the residual signal, the proposed algorithm not only considers the higher correlation atoms but also reserves the lower correlation atoms with the residual signal. In the lower correlation atoms, only a few are correct which usually impact the reconstructive performance and decide the reconstruction dynamic range of greedy pursuit methods. The others are redundant. In order to avoid redundant atoms impacting the reconstructive accuracy, the Bayesian pursuit algorithm is used to eliminate them. Simulation results show that the proposed algorithm can improve the reconstructive dynamic range and the reconstructive accuracy. Furthermore, better noise immunity compared with the existing greedy pursuit methods can be obtained.A new iterative algorithm is proposed to reconstruct an unknown sparse signal from a set of projected measurements. Unlike existing greedy pursuit methods which only consider the atoms having the highest correlation with the residual signal, the proposed algorithm not only considers the higher correlation atoms but also reserves the lower correlation atoms with the residual signal. In the lower correlation atoms, only a few are correct which usually impact the reconstructive performance and decide the reconstruction dynamic range of greedy pursuit methods. The others are redundant. In order to avoid redundant atoms impacting the reconstructive accuracy, the Bayesian pursuit algorithm is used to eliminate them. Simulation results show that the proposed algorithm can improve the reconstructive dynamic range and the reconstructive accuracy. Furthermore, better noise immunity compared with the existing greedy pursuit methods can be obtained.

关 键 词：compressive sensing （CS） correlation atom Bayesian hypothesis sparse reconstruction. 

分 类 号：TN911.7[电子电信—通信与信息系统]