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http://dx.doi.org/10.7471/ikeee.2013.17.3.339

Probabilistic Exclusion Based Orthogonal Matching Pursuit Algorithm for Sparse Signal Reconstruction  

Kim, Seehyun (Department of Information and Communication Engineering, The University of Suwon)
Publication Information
Journal of IKEEE / v.17, no.3, 2013 , pp. 339-345 More about this Journal
Abstract
In this paper, the probabilistic exclusion based orthogonal matching pursuit (PEOMP) algorithm for the sparse signal reconstruction is proposed. Some of recent greedy algorithms such as CoSaMP, gOMP, BAOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. They still often fail to converge to the solution because the support set could not escape from a local minimum. PEOMP helps to escape by excluding a random atom in the support set according to a well-chosen probability function. Experimental results show that PEOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.
Keywords
sparse signal reconstruction; compressive sensing; orthogonal matching pursuit (OMP); greedy algorithm; probabilistic exclusion;
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Times Cited By KSCI : 1  (Citation Analysis)
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