TY - JOUR
T1 - Generalized orthogonal matching pursuit
AU - Wang, Jian
AU - Kwon, Seokbeop
AU - Shim, Byonghyo
N1 - Funding Information:
Manuscript received March 28, 2012; revised July 20, 2012; accepted September 06, 2012. Date of publication September 13, 2012; date of current version November 20, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Lawrence Carin. This work was supported by the KCC (Korea Communications Commission), Korea, under the R&D program supervised by the KCA (Korea Communication Agency) (KCA-12-911-01-110) and the NRF grant funded by the Korea government (MEST) (No. 2011-0012525). This paper was presented in part at the Asilomar Conference on Signals, Systems and Computers, November 2011.
PY - 2012
Y1 - 2012
N2 - 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 δNK< √ N/√K+3√N. We also demonstrate by empirical simulations that the gOMP has excellent recovery performance comparable to L1- minimization technique with fast processing speed and competitive computational complexity.
AB - 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 δNK< √ N/√K+3√N. We also demonstrate by empirical simulations that the gOMP has excellent recovery performance comparable to L1- minimization technique with fast processing speed and competitive computational complexity.
KW - Compressive sensing (CS)
KW - orthogonal matching pursuit
KW - restricted isometry property (RIP)
KW - sparse recovery
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U2 - 10.1109/TSP.2012.2218810
DO - 10.1109/TSP.2012.2218810
M3 - Article
AN - SCOPUS:84870506486
VL - 60
SP - 6202
EP - 6216
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 12
M1 - 6302206
ER -