Performance of orthogonal matching pursuit for multiple measurement vectors with noise

Yan Wang, Tuo Fu, Meiguo Gao, Shuai Ding

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

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 ℓ2 norm of any nonzero row vector from the sparse matrix, exact support recovery of sparse matrix can be guaranteed.

Original languageEnglish
Title of host publication2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
Pages67-71
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Beijing, China
Duration: 6 Jul 201310 Jul 2013

Publication series

Name2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings

Conference

Conference2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013
Country/TerritoryChina
CityBeijing
Period6/07/1310/07/13

Keywords

  • Orthogonal matching pursuit (OMP)
  • bounded noise
  • coherence
  • exact support recovery
  • multiple measurement vectors (MMV)

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