Open AccessSparse signal recovery from noisy measurements via searching forward OMP
Author(s) -
Sun Quan,
Wu FeiYun,
Yang Kunde,
Huang Chunlong
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12365
Subject(s) - matching pursuit , compressed sensing , computer science , signal reconstruction , residual , algorithm , signal recovery , norm (philosophy) , basis pursuit , signal (programming language) , computational complexity theory , set (abstract data type) , noise (video) , pattern recognition (psychology) , artificial intelligence , signal processing , telecommunications , radar , political science , law , image (mathematics) , programming language
Recovering sparse signals from compressed measurements has received much attention in recent years. Considering that measurement errors always exist, an improved orthogonal matching pursuit (OMP) method which is called Searching Forward OMP (SFOMP), is proposed in this letter. The proposed SFOMP method is designed for compressive sensing and sparse signal recovery in the noisy environment. To improve the recovery performance, the SFOMP method incorporates a searching forward strategy to find the column leading to a minimum norm of residual error among the added candidates in each iteration. Numerical results show that, compared with other commonly used methods, this method provides a higher recovery signal‐to‐noise ratio, more accurate reconstruction of support set, and a competitive computational complexity with noisy measurements.