A Normalized Subband Adaptive Filter with Combined Regularization Parameter

被引：0
|
作者：
Shi, Long
Zhao, Haiquan [1 ]
机构：
[1] Southwest Jiaotong Univ, Key Lab Magnet Suspens Technol & Maglev Vehicle, Minist Educ, Chengdu 610031, Sichuan, Peoples R China
来源：
IFAC PAPERSONLINE | 2019年 / 52卷 / 24期
基金：
美国国家科学基金会;
关键词：
subband adaptive filter; mixing parameter; regularization parameter; fast convergence; misalignment; VARIABLE REGULARIZATION; ALGORITHM; NLMS;
D O I：
10.1016/j.ifacol.2019.12.399
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Proposed is a novel variable regularization algorithm for normalized subband adaptive filter (NSAF) by employing the mixing parameter to combine a large regularization parameter with a small regularization parameter. The mixing parameter is derived by minimizing the energy of noise-free a posteriori subband error. The combined regularization parameter can vary from a small value to a large value. Therefore, the proposed algorithm exhibits fast convergence rate meanwhile obtains low steady-state misalignment. Experimental simulations demonstrate the effectiveness of our work. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：158 / 162
页数：5
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