Decentralized and Collaborative Subspace Pursuit: A Communication-Efficient Algorithm for Joint Sparsity Pattern Recovery With Sensor Networks

In this paper, we consider the problem of joint sparsity pattern recovery in a distributed sensor network. The sparse multiple measurement vector signals (MMVs) observed by all the nodes are assumed to have a common (but unknown) sparsity pattern. To accurately recover the common sparsity pattern in...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 64; no. 3; pp. 556 - 566
Main Authors: Gang Li, Wimalajeewa, Thakshila, Varshney, Pramod K.
Format: Journal Article
Language:English
Published: IEEE 01.02.2016
Subjects:
Algorithm design and analysis
Algorithms
Collaboration
compressive sensing
Decentralized
Estimates
information fusion
Joint sparsity pattern recovery
Matching pursuit algorithms
Mathematical analysis
Networks
Recovery
Signal processing algorithms
Sparse representation
subspace pursuit
Subspaces
Vectors (mathematics)
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:In this paper, we consider the problem of joint sparsity pattern recovery in a distributed sensor network. The sparse multiple measurement vector signals (MMVs) observed by all the nodes are assumed to have a common (but unknown) sparsity pattern. To accurately recover the common sparsity pattern in a decentralized manner with a low communication overhead of the network, we develop an algorithm named decentralized and collaborative subspace pursuit (DCSP). In DCSP, each node is required to perform three kinds of operations per iteration: 1) estimate the local sparsity pattern by finding the subspace that its measurement vector most probably lies in; 2) share its local sparsity pattern estimate with one-hop neighboring nodes; and 3) update the final sparsity pattern estimate by majority vote based fusion of all the local sparsity pattern estimates obtained in its neighborhood. The convergence of DCSP is proved and its communication overhead is quantitatively analyzed. We also propose another decentralized algorithm named generalized DCSP (GDCSP) by allowing more information exchange among neighboring nodes to further improve the accuracy of sparsity pattern recovery at the cost of increased communication overhead. Experimental results show that, 1) compared with existing decentralized algorithms, DCSP provides much better accuracy of sparsity pattern recovery at a comparable communication cost; and 2) the accuracy of GDCSP is very close to that of centralized processing.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2015.2483482