Block sparse reweighted zero-attracting normalised least mean square algorithm for system identification

被引：3
|
作者：
Yan, Zhenhai [1 ,2 ]
Yang, Feiran [1 ]
Yang, Jun [1 ,2 ]
机构：
[1] Chinese Acad Sci, Inst Acoust, Key Lab Noise & Vibrat Res, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
来源：
ELECTRONICS LETTERS | 2017年 / 53卷 / 14期
关键词：
NORM; LMS;
D O I：
10.1049/el.2017.1115
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：899 / 900
页数：2
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