%0 Journal Article
%A 丁寅
%A 束丰
%A 张玲华
%T Iterative subspace matching pursuit for joint sparse recovery
%D 2023
%R 10.19682/j.cnki.1005-8885.2022.0021
%J 中国邮电高校学报（英文）
%P 26-35
%V 30
%N 2
%X <div style="text-align:justify;">
	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.
</div>
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2022.0021