Open AccessStrong recovery conditions for least support orthogonal matching pursuit in noisy case
Author(s) -
Tawfic I.S.,
Koç Kayhan S.
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2015.0222
Subject(s) - matching pursuit , restricted isometry property , isometry (riemannian geometry) , property (philosophy) , noise reduction , signal reconstruction , algorithm , noise (video) , computer science , signal (programming language) , matching (statistics) , pattern recognition (psychology) , signal to noise ratio (imaging) , artificial intelligence , signal recovery , compressed sensing , mathematics , signal processing , statistics , image (mathematics) , telecommunications , philosophy , radar , epistemology , pure mathematics , programming language
A least support denoising‐orthogonal matching pursuit (LSD‐OMP) algorithm to reconstruct the sparse signal using less number of iterations from noisy measurements is presented. The algorithm achieves correct support recovery without requiring sparsity knowledge. An improved restricted isometry property‐based condition is derived over the best‐known results. Experimental results demonstrate that the LSD‐OMP achieves good performance on recovering sparse signals, outperforming the latest state‐of‐the art method in terms of reconstructed signal‐to‐noise ratio and running time.