NDLI: Performance of orthogonal matching pursuit for multiple measurement vectors with noise

Content Provider	IEEE Xplore Digital Library
Author	Yan Wang Tuo Fu Meiguo Gao Shuai Ding
Copyright Year	2013
Description	Author affiliation: Radar Res. Lab., Beijing Inst. of Technol., Beijing, China (Yan Wang; Tuo Fu; Meiguo Gao; Shuai Ding)
Abstract	Orthogonal matching pursuit (OMP) algorithm for the multiple measurement vectors (MMV) is a greedy method to find the sparse matrix with few nonzero rows that represents the measurement vectors under the sensing matrix. This paper analyzes the recovery performance of OMP for MMV (OMPMMV) in the bounded noise scenarios, and provides the sufficient conditions that are related to the sensing matrix and sparse matrix for exact support recovery. We start with the intuitive sufficient conditions for exact support recovery, and then apply these conditions to scenarios of two types of bounded noise. The results show that under some conditions on the coherence of the sensing matrix and the minimum $ℓ_{2}$ norm of any nonzero row vector from the sparse matrix, exact support recovery of sparse matrix can be guaranteed.
Starting Page	67
Ending Page	71
File Size	246655
Page Count	5
File Format	PDF
ISBN	9781479910434
DOI	10.1109/ChinaSIP.2013.6625299
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sufficient conditions Orthogonal matching pursuit (OMP) Exact support recovery Noise Matching pursuit algorithms Bounded noise Multiple measurement vectors (MMV) Coherence Vectors Sensors Sparse matrices Noise measurement
Content Type	Text
Resource Type	Article

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