Open AccessImproved sparse signal recovery method based on matching pursuit revised MAP estimation
Author(s) -
Pan Jian,
Tang Jun,
Wang Li,
Zhang Yunlei
Publication year - 2018
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.2018.0727
Subject(s) - matching pursuit , computer science , signal (programming language) , noise (video) , matching (statistics) , statistic , bayesian probability , pattern recognition (psychology) , artificial intelligence , prior information , algorithm , mathematics , statistics , compressed sensing , image (mathematics) , programming language
The problem of recovering a sparse signal buried in noise from a linear observing system has been studied, via utilising statistic prior information of noise and signal, a fast matching pursuit revised maximum a posterior (MP‐RMAP) estimation method is proposed. The proposed method overcomes the none‐robust estimation problem that fast Bayesian matching pursuit has in high signal‐to‐noise ratio cases. Compared with other methods, the MP‐RMAP method is able to improve the estimation accuracy with low complexity. Experimental results verify the validity of the proposed algorithm.