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On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Computer Graphics and Image Processing)
Qualcomm Technologies Inc., San Jose, CA, USA.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Computer Graphics and Image Processing)ORCID iD: 0000-0002-7765-1747
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. (Ultra high-speed Nonlinear Integrated Circuit (UNIC))
2017 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, no 11, p. 1646-1650Article in journal (Refereed) Published
Abstract [en]

In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pursuit (OMP) algorithm. A lower bound for the probability of correctly identifying the support of a sparse signal with additive white Gaussian noise is derived. Compared to previous work, the new bound takes into account the signal parameters such as dynamic range, noise variance, and sparsity. Numerical simulations show significant improvements over previous work and a closer match to empirically obtained results of the OMP algorithm.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2017. Vol. 24, no 11, p. 1646-1650
Keyword [en]
Compressed Sensing (CS), Sparse Recovery, Orthogonal Matching Pursuit (OMP), Mutual Coherence
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-141613DOI: 10.1109/LSP.2017.2753939ISI: 000412501600001OAI: oai:DiVA.org:liu-141613DiVA, id: diva2:1146543
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2017-10-23Bibliographically approved

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