Numer. Math. Theor. Meth. Appl., 9 (2016), pp. 185-192.
Published online: 2016-09
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In this paper, a new stopping rule is proposed for orthogonal multi-matching pursuit (OMMP). We show that, for $ℓ_2$ bounded noise case, OMMP with the new stopping rule can recover the true support of any $K$-sparse signal $x$ from noisy measurements $y = Φx + e$ in at most $K$ iterations, provided that all the nonzero components of $x$ and the elements of the matrix $Φ$ satisfy certain requirements. The proposed method can improve the existing result. In particular, for the noiseless case, OMMP can exactly recover any $K$-sparse signal under the same RIP condition.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2016.m1424}, url = {http://global-sci.org/intro/article_detail/nmtma/12373.html} }In this paper, a new stopping rule is proposed for orthogonal multi-matching pursuit (OMMP). We show that, for $ℓ_2$ bounded noise case, OMMP with the new stopping rule can recover the true support of any $K$-sparse signal $x$ from noisy measurements $y = Φx + e$ in at most $K$ iterations, provided that all the nonzero components of $x$ and the elements of the matrix $Φ$ satisfy certain requirements. The proposed method can improve the existing result. In particular, for the noiseless case, OMMP can exactly recover any $K$-sparse signal under the same RIP condition.