Improving the Bound on the RIP Constant in Generalized Orthogonal Matching Pursuit

Improving the Bound on the RIP Constant in Generalized Orthogonal Matching Pursuit

The generalized Orthogonal Matching Pursuit (gOMP) is a recently proposed compressive sensing greedy recovery algorithm which generalizes the OMP algorithm by selecting N( ≥ 1) atoms in each iteration. In this letter, we demonstrate that the gOMP can successfully reconstruct a K-sparse signal from a...

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Veröffentlicht in:IEEE signal processing letters 2013-11, Vol.20 (11), p.1074-1077
Hauptverfasser: Satpathi, Siddhartha, Das, Rajib Lochan, Chakraborty, Mrityunjoy
Format: Artikel
Sprache:eng
Schlagworte:
Compressed sensing
Compressive sensing
Convergence
Indexes
Matching pursuit algorithms
orthogonal matching pursuit
restricted isometry property
sensing matrix
Sensors
Upper bound
Vectors
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Zusammenfassung:The generalized Orthogonal Matching Pursuit (gOMP) is a recently proposed compressive sensing greedy recovery algorithm which generalizes the OMP algorithm by selecting N( ≥ 1) atoms in each iteration. In this letter, we demonstrate that the gOMP can successfully reconstruct a K-sparse signal from a compressed measurement y=Φx by a maximum of K iterations if the sensing matrix Φ satisfies the Restricted Isometry Property (RIP) of order NK, with the RIP constant δ NK satisfying δ NK
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2013.2279977