Open AccessCauchy diversity measures: a novel methodology for enhancing sparsity in compressed sensing
Author(s) -
Zhao Guanghui,
Shen Fangfang,
Wang Zhengyang,
Shi Guangming
Publication year - 2013
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2012.0329
Subject(s) - compressed sensing , minimisation (clinical trials) , cauchy distribution , norm (philosophy) , exploit , mathematical optimization , mathematics , computer science , algorithm , statistics , computer security , political science , law
As a new enchanting theory, compressed sensing (CS) demonstrates that a sparse signal can be recovered through a surprisingly small number of linear measurements by solving a problem of ℓ 1 norm minimisation (which can be thought as a special case of the signomial diversity measures). However, the traditional CS model with ℓ 1 norm minimisation can not fully exploit the sparsity especially when the degree of sparsity increases or the measurements number reduces. In this study, the Cauchy diversity measures is incorporated into the proposed model to deal with the above difficulties. The simulation results demonstrate that under the same condition, this new model offers a superior reconstruction precision compared with the common used signomial diversity measures.