一种变正则化矩阵的改进多带结构子带自适应滤波算法

doi:10.3969/j.issn.0372-2112.2017.08.030

电子学报 ›› 2017, Vol. 45 ›› Issue (8): 2019-2025.DOI: 10.3969/j.issn.0372-2112.2017.08.030

一种变正则化矩阵的改进多带结构子带自适应滤波算法

闫震海^1,2, 杨飞然¹, 杨军^1,2

1. 中国科学院噪声与振动重点实验室(声学研究所), 北京 100190;
2. 中国科学院大学, 北京 100049

收稿日期:2016-04-11 修回日期:2016-10-08 出版日期:2017-08-25
- 通讯作者:
- 杨军
- 作者简介:
- 闫震海,男,1988年生,现为中国科学院声学研究所在读博士生,主要研究方向为自适应信号处理和回声抵消.E-mail:yanzhenhai@mail.ioa.ac.cn;杨飞然,男,1982年生,现为中国科学院声学研究所副研究员,主要研究方向为自适应信号处理、回声抵消、语音增强和3D音频系统.E-mail:feirany.ioa@gmail.com
- 基金资助:
- 国家自然科学基金 (No.61501449，No.11404367）; 中国科学院先导专项项目 (No.XDA06040501）

A Variable Regularization Matrix for the Lmproved Multiband-Structured Subband Adaptive Filter

YAN Zhen-hai^1,2, YANG Fei-ran¹, YANG Jun^1,2

1. Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Acaderny of Sciences, Beijing 100049, China

Received:2016-04-11 Revised:2016-10-08 Online:2017-08-25 Published:2017-08-25
- Supported by:
- National Natural Science Foundation of China (No.61501449, No.11404367); Pilot Project of Chinese Academy of Sciences (No.XDA06040501)

摘要/Abstract

摘要： 定正则化因子的改进多带结构子带自适应滤波（IMSAF）算法在取得收敛速度快和稳态失调误差小之间存在冲突.根据系统噪声抵消原理，设定子带后验误差功率等于子带噪声功率，本文提出了变正则化矩阵的IMSAF算法来解决这一问题.仿真结果证明，所提算法可以同时达到收敛速度快、稳态失调误差小以及追踪速度快等优势.

关键词: 自适应滤波, 改进的多带结构子带自适应滤波器, 变正则化矩阵

Abstract: The conflict between fast convergence rate and small steady-state misadjustment is a problem of the improved multiband-structured subband adaptive filter (IMSAF) algorithm with a fixed regularization factor.Based on the principle of minimizing the system noise,the power of the subband posteriori error is assumed to equal the power of the subband noise.Under this assumption a variable regularization matrix for the IMSAF algorithm is proposed to solve this problem.Simulation results show that the proposed method is superior to the IMSAF algorithm in terms of convergence rate,steady-state misadjustment and tracking capability.

Key words: adaptive filtering, improved multiband-structured subband adaptive filter (IMSAF), variable regularization matrix

中图分类号:

TN911.72

闫震海, 杨飞然, 杨军. 一种变正则化矩阵的改进多带结构子带自适应滤波算法[J]. 电子学报, 2017, 45(8): 2019-2025.

YAN Zhen-hai, YANG Fei-ran, YANG Jun . A Variable Regularization Matrix for the Lmproved Multiband-Structured Subband Adaptive Filter[J]. Acta Electronica Sinica, 2017, 45(8): 2019-2025.

参考文献

[1] Pradham S S,Reddy V U.A new approach to subband adaptive filtering[J].IEEE Transactions on Signal Processing,1999,47(3):655-664.
[2] Lee K A,Gan W S,Kuo S M.Subband Adaptive Filtering:Theory and Implementation[M].John Wiley & Sons,2009.
[3] Lee K A,Gan W S.Improving convergence of the NLMS algorithm using constrained subband updates[J].IEEE signal processing letters,2004,11(9):736-739.
[4] Zheng Z S,Zhao H Q.Bias-compensated normalized subband adaptive filter algorithm[J].IEEE Signal Processing Letters,2016,23(6):809-813.
[5] Yu Y,Zhao H Q,Chen B D.Steady-state mean-square-deviation analysis of the sign subband adaptive filter algorithm[J].Signal Processing,2016,120:36-42.
[6] Yang F R,Wu M,Ji P F,et al.An improved multiband-structured subband adaptive filter algorithm[J].IEEE Signal Processing Letters,2012,19(10):647-650.
[7] Haykin S S.Adaptive Filter Theory[M].Pearson Education India,2008.
[8] Ozeki K,Umeda T.An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties[J].Electronics and Communications in Japan (Part I:Communications),1984,67(5):19-27.
[9] Yang F R,Wu M,Ji P F,et al.Transient and steady-state analyses of the improved multiband-structured subband adaptive filter algorithm[J].IET Signal Processing,2015,9(8):596-604.
[10] Yin W T,Mehr A S.A variable regularization method for affine projection algorithm[J].IEEE Transactions on Circuits and Systems Ⅱ:Express Briefs,2010,57(6):476-480.
[11] Paleologu C,Benesty J,Ciochinǎ S.A practical solution for the regularization of the affine projection algorithm[A].Proceedings of the 10th International Conference on Communications (COMM)[C].Bucharest,Romania:IEEE,2014.1-4.
[12] Yu Y,Zhao H Q,Chen B D.A new normalized subband adaptive filter algorithm with individual variable step sizes[J].Circuits,Systems,and Signal Processing,2016,35(4):1407-1418.
[13] Yu Y,Zhao H Q,He Z Y,et al.A robust band-dependent variable step size NSAF algorithm against impulsive noises[J].Signal Processing,2016,119:203-208.
[14] Benesty J,Rey H,Vega L R,et al.A nonparametric vss nlms algorithm[J].IEEE Signal Processing Letters,2006,13(10):581-584.
[15] Ni J G,Li F.A variable regularization matrix normalized subband adaptive filter[J].IEEE Signal Processing Letters,2009,16(2):105-108.
[16] Rey H,Vega L R,Tressens S,et al.Variable explicit regularization in affine projection algorithm:Robustness issues and optimal choice[J].IEEE Transactions on Signal Processing,2007,55(5):2096-2109.
[17] Yang F R,Wu M,Ji P F,et al.Low-complexity implementation of the improved multiband-structured subband adaptive filter algorithm[J].IEEE Transactions on Signal Processing,2015,63(19):5133-5148.
[18] Lee K A,Gan W S,Kuo S M.Mean-square performance analysis of the normalized subband adaptive filter[A].Proceedings of the Fortieth Asilomar Conference on Signals,Systems and Computers[C].Pacific Grove,America:IEEE,2006.248-252.
[19] Ciochinǎ S,Paleologu C,Benesty J,et al.An optimized NLMS algorithm for acoustic echo cancellation[A].Proceedings of the 2015 International Symposium on Signals,Circuits and Systems (ISSCS)[C].Iasi,Romania:IEEE,2015.1-4.

一种变正则化矩阵的改进多带结构子带自适应滤波算法

A Variable Regularization Matrix for the Lmproved Multiband-Structured Subband Adaptive Filter

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