The Journal of China Universities of Posts and Telecommunications ›› 2023, Vol. 30 ›› Issue (2): 26-35.doi: 10.19682/j.cnki.1005-8885.2022.0021

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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


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.

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

CLC Number: