Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction

Xingyi Chen, Yujie Zhang and Rui Qi
Volume: 15, No: 2, Page: 410 ~ 421, Year: 2019
10.3745/JIPS.04.0111
Keywords: Block Sparse Signals, Compressed Sensing, Distributed Compressed Sensing, Iteration Algorithm
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Abstract
Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

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Cite this article
IEEE Style
X. Chen, Y. Zhang and R. Qi, "Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction ," Journal of Information Processing Systems, vol. 15, no. 2, pp. 410~421, 2019. DOI: 10.3745/JIPS.04.0111.

ACM Style
Xingyi Chen, Yujie Zhang, and Rui Qi. 2019. Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction , Journal of Information Processing Systems, 15, 2, (2019), 410~421. DOI: 10.3745/JIPS.04.0111.