Iterative subspace matching pursuit for joint sparse recovery

doi:10.19682/j.cnki.1005-8885.2022.0021

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (2): 26-35.doi: 10.19682/j.cnki.1005-8885.2022.0021

Iterative subspace matching pursuit for joint sparse recovery

束丰,张玲华,丁寅

南京邮电大学

收稿日期:2021-12-15 修回日期:2022-07-02 出版日期:2023-04-30 发布日期:2023-04-27
通讯作者: 张玲华 E-mail:zhanglh@njupt.edu.cn;y080627@njupt.edu.cn
基金资助:
中国国家自然科学基金;江苏省研究生科研与实践创新计划

Iterative subspace matching pursuit for joint sparse recovery

Shu Feng, Zhang Linghua , Ding Yin

Received:2021-12-15 Revised:2022-07-02 Online:2023-04-30 Published:2023-04-27
Supported by:
National Natural Science Foundation of China;Postgraduate Research and Practice Innovation Program of Jiangsu Province

摘要/Abstract

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)

Abstract:

Key words: joint sparse recovery (JSR)| multiple measurement vector (MMV)| support set estimation| compressed sensing (CS)

中图分类号:

TN911.7

Shu Feng, Zhang Linghua , Ding Yin. Iterative subspace matching pursuit for joint sparse recovery[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(2): 26-35.

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Iterative subspace matching pursuit for joint sparse recovery

Iterative subspace matching pursuit for joint sparse recovery

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