Open AccessAdaptive selective compressive sampling for sparse signal acquisition in noisy background
Author(s) -
Wen Fangqing,
Zhang Yu,
Zhang Gong
Publication year - 2015
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/el.2015.2480
Subject(s) - compressed sensing , signal (programming language) , computer science , noise (video) , sampling (signal processing) , signal reconstruction , sparse matrix , prior information , detection theory , signal recovery , matrix (chemical analysis) , algorithm , signal processing , artificial intelligence , computer vision , telecommunications , physics , materials science , radar , filter (signal processing) , quantum mechanics , composite material , detector , image (mathematics) , gaussian , programming language
A new methodology for sparse signal acquisition using adaptive selective compressive sampling (ASCS) is presented. By employing the estimated prior information, the measurement matrix in the ASCS method can be adapted in order to selectively sense the sparse signal. The proposed ASCS method has an inherent characteristic of noise suppression, thus provides fewer noisy measurements. The experimental results demonstrate the effectiveness of the proposed scheme.