Robust Constrained Normalized M-Estimate Subband Adaptive Filter: Algorithm Derivation and Performance Analysis

被引：0
|
作者：
Xu, Wenjing [1 ,2 ]
Zhao, Haiquan [1 ,2 ]
Lv, Shaohui [1 ,2 ]
机构：
[1] Southwest Jiaotong Univ, Key Lab Magnet Suspens Technol & Maglev Vehicle, Minist Educ, Chengdu 610031, Peoples R China
[2] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2024年 / 71卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Vectors; Adaptive filters; Filtering algorithms; Convergence; Computational complexity; Performance analysis; Optimization; Constrained adaptive filter; colored input; subband adaptive filter; M-estimate; sparse system;
D O I：
10.1109/TCSII.2024.3373230
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：
This brief presents a constrained normalized M-estimate subband adaptive filter (CNMSAF) algorithm that is robust to impulsive noise, which utilizes subband decomposition technique to whiten the colored input signals thereby achieving fast convergence speed and low computational complexity. In addition, the stability and theoretical mean square deviation performance of the algorithm are analyzed. In order to solve the constrained filtering problem with sparsity, the L-1 norm of the filter weight vector is used as an additional constraint, and we also propose the L-1 -CNMSAF algorithm. Computer simulations verify the accuracy of the theoretical analysis and the effectiveness of the proposed algorithms.
引用
收藏
页码：4010 / 4014
页数：5
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