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Backtracking-based matching pursuit method for distributed compressed sensing

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Abstract

In this paper, we study a distributed compressed sensing (DCS) problem in which we need to recover a set of jointly sparse vectors from the measurements. A Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) method to approximately sparse solutions for DCS is proposed. It is an iterative approach where each iteration consists of consecutive forward selection to adaptively choose several atoms and backward removal stages to detect the previous chosen atoms’ reliability and then delete the unreliable atoms at each iteration. Also, unlike its several predecessors, the proposed method does not require the sparsity level to be known as a prior which makes it a potential candidate for many practical applications, when the sparsity of signals is not available. We demonstrate the reconstruction ability of the proposed algorithm on both synthetically generated data and image using Normal and Binary sparse signals, and the real-life electrocardiography (ECG) data, where the proposed method yields less reconstruction error and higher exact recovery rate than other existing DCS algorithms.

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Acknowledgments

This work is supported by Natural Science Foundation of China (No. 61302138) and Youth Foundation of Naval University of Engineering (No.HGDQNJJ13005).

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Authors and Affiliations

  1. School of Mathematics and Physics, China University of Geosciences, Wuhan, China

    Yujie Zhang, Rui Qi & Yanni Zeng

  2. School of Computer Science, University of Windsor, Windsor, ON, Canada

    Yujie Zhang

  3. School of Science, Naval University of Engineering, Wuhan, China

    Rui Qi

  4. Faculty of Statistics, Hubei University of Economics, Wuhan, China

    Yanni Zeng

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  1. Yujie Zhang
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  2. Rui Qi
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Correspondence to Yujie Zhang.

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Zhang, Y., Qi, R. & Zeng, Y. Backtracking-based matching pursuit method for distributed compressed sensing. Multimed Tools Appl 76, 14691–14710 (2017). https://doi.org/10.1007/s11042-016-3933-x

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  • DOI: https://doi.org/10.1007/s11042-016-3933-x

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