Open AccessBlock sparse reweighted zero‐attracting normalised least mean square algorithm for system identification
Author(s) -
Yan Zhenhai,
Yang Feiran,
Yang Jun
Publication year - 2017
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.2017.1115
Subject(s) - partition (number theory) , block (permutation group theory) , algorithm , context (archaeology) , convergence (economics) , mathematics , computer science , zero (linguistics) , combinatorics , paleontology , economics , biology , economic growth , linguistics , philosophy
To improve the performance for identifying the block sparse system, a block sparse reweighted zero‐attracting normalised least mean square algorithm (NLMS) (BS‐RZA‐NLMS) is proposed in this Letter. The proposed algorithm is derived by applying block sparsity constraint on the cost function of the NLMS, which is a log‐sum penalty of adaptive tap weights with equal block partition sizes. The convergence behaviour of the BS‐RZA‐NLMS is analysed in terms of the zero attraction and block partition. Simulation results demonstrate the performance advantage of the proposed algorithm in the context of block sparse system identification.