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Block Sparse Signal Recovery in Compressed Sensing: Optimum Active Block Selection and Within-Block Sparsity Order Estimation

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Abstract

In this paper, we develop a new algorithm for recovery of block sparse signals in compressed sensing framework based on orthogonal matching pursuit. Furthermore, we point out that a major issue in conventional sparse signal recovery is the lack of prior knowledge about the order of sparsity. Consequently, block sparse signal recovery algorithms suffer even more from the same problem since two parameters are needed for exact recovery of the signal, order of active blocks and within-block sparsity order. Therefore, we propose a new approach to determining both of the active blocks and within-block sparsity order which is embedded in the proposed block sparse signal recovery algorithm. The simulation results illustrate the improved performance of the proposed method for recovery of block sparse signals compared to the conventional methods which are not aware of the prior information. We also apply our proposed algorithm to ECG signal compression, where the obtained results reveal its efficiency.

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  1. Available online at http://www.physionet.org/physiobank/database/ptbdb/.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 51664, Iran

    Mahdi Shamsi, Tohid Yousefi Rezaii & Mohammad Ali Tinati

  2. Department of Electrical Engineering, Malayer University, Malayer, 65719-95863, Iran

    Amir Rastegarnia & Azam Khalili

Authors
  1. Mahdi Shamsi
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  2. Tohid Yousefi Rezaii
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  3. Mohammad Ali Tinati
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  4. Amir Rastegarnia
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  5. Azam Khalili
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Correspondence to Tohid Yousefi Rezaii.

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Shamsi, M., Yousefi Rezaii, T., Tinati, M.A. et al. Block Sparse Signal Recovery in Compressed Sensing: Optimum Active Block Selection and Within-Block Sparsity Order Estimation. Circuits Syst Signal Process 37, 1649–1668 (2018). https://doi.org/10.1007/s00034-017-0617-3

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  • DOI: https://doi.org/10.1007/s00034-017-0617-3

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