Generalized orthogonal matching pursuit

Jian Wang, Seokbeop Kwon, Byonghyo Shim

Research output: Contribution to journalArticle

339 Citations (Scopus)

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 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.

Original languageEnglish
Article number6302206
Pages (from-to)6202-6216
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume60
Issue number12
DOIs
Publication statusPublished - 2012

Keywords

  • Compressive sensing (CS)
  • orthogonal matching pursuit
  • restricted isometry property (RIP)
  • sparse recovery

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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