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Forward-backward pursuit method for distributed compressed sensing

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Abstract

In this paper, a forward-backward pursuit method for distributed compressed sensing (DCSFBP) is proposed. In contrast to existing distributed compressed sensing (DCS), it is an adaptive iterative approach where each iteration consists of consecutive forward selection and backward removal stages. And it not needs sparsity as prior knowledge and multiple indices are identified at each iteration for recovery. These make it a potential candidate for many practical applications, when the sparsity of signals is not available. Numerical experiments, including recovery of random sparse signals with different nonzero coefficient distributions in many scenarios, in addition to the recovery of sparse image and the real-life electrocardiography (ECG) data, are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing DCS algorithms.

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Acknowledgments

This work is supported by Natural Science Foundation of China (No. 61302138 and No.61601417) and Youth Foundation of Naval University of Engineering (No.HGDQNJJ13005). The authors would like to thank the anonymous reviewers for their thorough reading of the paper, and patient feedback.

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Authors and Affiliations

  1. School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China

    Yujie Zhang, Rui Qi & Yanni Zeng

  2. School of Computer Science, University of Windsor, Windsor, ON, N9B 3P4, Canada

    Yujie Zhang

  3. School of Science, Naval University of Engineering, Wuhan, 430033, China

    Rui Qi

  4. Faculty of Statistics, Hubei University of Economics, Wuhan, 430070, China

    Yanni Zeng

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  1. Yujie Zhang
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  2. Rui Qi
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  3. Yanni Zeng
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Correspondence to Yujie Zhang.

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Zhang, Y., Qi, R. & Zeng, Y. Forward-backward pursuit method for distributed compressed sensing. Multimed Tools Appl 76, 20587–20608 (2017). https://doi.org/10.1007/s11042-016-3968-z

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  • DOI: https://doi.org/10.1007/s11042-016-3968-z

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