Generalized Orthogonal Matching Pursuit
- Authors
- Wang, Jian; Kwon, Seokbeop; Shim, Byonghyo
- Issue Date
- 12월-2012
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Compressive sensing (CS); orthogonal matching pursuit; restricted isometry property (RIP); sparse recovery
- Citation
- IEEE TRANSACTIONS ON SIGNAL PROCESSING, v.60, no.12, pp.6202 - 6216
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Volume
- 60
- Number
- 12
- Start Page
- 6202
- End Page
- 6216
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/106712
- DOI
- 10.1109/TSP.2012.2218810
- ISSN
- 1053-587X
- Abstract
- As a greedy algorithm to recover sparse signals from compressed measurements, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we introduce an extension of the OMP for pursuing efficiency in reconstructing sparse signals. Our approach, henceforth referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple N indices are identified per iteration. Owing to the selection of multiple "correct" indices, the gOMP algorithm is finished with much smaller number of iterations when compared to the OMP. We show that the gOMP can perfectly reconstruct any K-sparse signals (K > 1), provided that the sensing matrix satisfies the RIP with delta(NK) < root N/root K+3 root N. We also demonstrate by empirical simulations that the gOMP has excellent recovery performance comparable to l(1)-minimization technique with fast processing speed and competitive computational complexity.
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