Iterative subspace matching pursuit for joint sparse recovery  

作　　者：Shu Feng Zhang Linghua Ding Yin 

机构地区：[1]School of Communications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2023年第2期26-35,共10页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61771258);the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX 210749)。

摘　　要：Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other.First,an iterative atom refinement process is adopted to estimate part of the atoms of the support set.Subsequently,the above atoms along with the measurements are used to estimate the remaining atoms.The estimation criteria for atoms are based on the principle of minimum subspace distance.Extensive numerical experiments were performed in noiseless and noisy scenarios,and results reveal that iterative subspace matching pursuit(ISMP)outperforms other existing algorithms for JSR.

关 键 词：joint sparse recovery(JSR) multiple measurement vector(MMV) support set estimation compressed sensing(CS) 

分 类 号：TN929.5[电子电信—通信与信息系统]