NDLI: Coherence-based analysis of modified orthogonal matching pursuit using sensing dictionary

Content Provider	IET Digital Library
Author	Zhao, Juan Bai, Xia Bi, Shi He Tao, Ran
Abstract	Compressed sensing (CS) has attracted considerable attention in signal processing because of its advantage of recovering sparse signals with lower sampling rates than the Nyquist rates. Greedy pursuit algorithms such as orthogonal matching pursuit (OMP) are well-known recovery algorithms in CS. In this study, the authors study a modified OMP proposed by Schnass et al., which uses a special sensing dictionary to identify the support of a sparse signal while maintaining the same computational complexity. The performance guarantee of this modified OMP in recovering the support of a sparse signal is analysed in the framework of mutual (cross) coherence. Furthermore, they discuss the modified OMP in the case of bounded noise and Gaussian noise, and show that the performance of the modiﬁed OMP in the presence of noise relies on the mutual (cross) coherence and the minimum magnitude of the non-zero elements of the sparse signal. Finally, simulations are constructed to demonstrate the performance of the modified OMP.
Starting Page	218
Ending Page	225
Page Count	8
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 3, May (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0164
Journal	IET Signal Processing
Publisher Date	2015-04-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bounded Noise Coherence Coherence Based Analysis Compressed Sensing Cross Coherence Digital Signal Processing Gaussian Noise Greedy Pursuit Algorithm Interpolation And Function Approximation Iterative Method Modified Orthogonal Matching Pursuit Mutual Coherence Numerical Analysis Sensing Dictionary Signal Processing And Detection Signal Processing Theory Sparse Signal
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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