一种应对非平稳频率失调的窄带主动噪声控制系统

黄博妍; 常琳; 马亚平; 孙金玮; 魏国

doi:10.16383/j.aas.2015.c130797

一种应对非平稳频率失调的窄带主动噪声控制系统

doi: 10.16383/j.aas.2015.c130797

1.
哈尔滨工业大学电气工程及自动化学院哈尔滨 150001

基金项目:

国家自然科学基金(61171183;2012)航天支撑基金(01320214)资助

详细信息

作者简介:
黄博妍哈尔滨工业大学电气工程及自动化学院讲师.主要研究方向为信号与信息处理,主动噪声控制理论.E-mail:byhuang@hit.edu.cn

通讯作者:
魏国哈尔滨工业大学电气工程及自动化学院教授.主要研究方向为传感技术和自适应信号处理.本文通信作者.E-mail:wg_weiguo@yahoo.com.cn

计量
- 文章访问数: 1944
- HTML全文浏览量: 92
- PDF下载量: 790
- 被引次数: 0
出版历程
- 收稿日期: 2013-08-26
- 修回日期: 2014-03-14
- 刊出日期: 2015-01-20

A New Narrowband ANC System against Nonstationary Frequency Mismatch

1.
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001

Funds:

Supported by National Natural Science Foundation of China (61171183) and 2012 Aerospace Support Fund (01320214)

摘要

摘要: 在窄带主动噪声控制(Active noise control, ANC)系统中, 参考信号频率失调(Frequency mismatch, FM)和噪声信号非平稳将会使系统性能下降, 甚至失效. 本文提出一种基于动量最小均方的改进FM补偿算法, 通过在代价函数中引入加权累加的平方误差, 提升系统的追踪和收敛能力. 并分别与基于滤波-X 最小均方(Filtered -X least mean square, FXLMS)、滤波-X 递归最小二乘(Filtered -X recursive least square, FXRLS)和变步长滤波-X最小均方(Variable step-size filtered -X least mean square, VSS-FXLMS)算法的主控制系统结合, 共同完成系统综合性能的提高. 大量仿真分析表明, 新的FM补偿算法在非平稳的FM和离散傅里叶系数翻转的条件下仍能保持较高的追踪能力和合理的残余误差.
- 窄带主动噪声控制 /
- 频率失调补偿 /
- 非平稳噪声 /
- 动量最小均方 /
- 变步长滤波-X最小均方
Abstract: A frequency mismatch (FM) compensation method is proposed for the narrowband active noise control (ANC) system to solve the problem of large nonstationary FM. Inspired by the momentum least mean square (LMS) theory, a new recursion algorithm for the updating of frequency compensation sequence is derived by incorporating a weighted accumulated square error into the cost function, and applied to the conventional filtered -X least mean square (FXLMS), filtered -X recursive least square (FXRLS), and variable step-size filtered -X LMS (VSS-FXLMS) algorithms, respectively. In both stationary and nonstationary environments, extensive simulations show that the proposed FM compensation method combined with the VSS-FXLMS based main controller possesses excellent tracking ability and is fast convergent.

HTML全文

参考文献(21)

[1]	Xiao Y G, Tadokoro Y, Shida K. Adaptive algorithm based on least mean p-power error criterion for Fourier analysis in additive noise. IEEE Transactions on Signal Processing, 1999, 47(4): 1172-1181
[2]	Xiao Y G, Ikuta A, Ma L, Khorosani K. Stochastic analysis of the FXLMS-based narrowband active noise control system. IEEE Transactions on Audio, Speech, Language Processing, 2008, 16(5): 1000-1014
[3]	Wang L, Gan W S. Convergence analysis of narrowband active noise equalizer system under imperfect secondary path estimation. IEEE Transactions on Audio, Speech, Language Processing, 2009, 17(4): 566-571
[4]	Xiao Y, Ma L, Hasegawa K. Properties of FXLMS-based narrowband active noise control with online secondary-path modeling. IEEE Transactions on Signal Processing, 2009, 57(8): 2931-2949
[5]	Sun X, Kuo S M. Active narrowband noise control systems using cascading adaptive filters. IEEE Transactions on Audio, Speech, Language Processing, 2007, 15(2): 586-592
[6]	Huang B Y, Xiao Y G, Sun J W, Wei G. A variable step-size FXLMS algorithm for narrowband active noise control. IEEE Transactions on Audio, Speech, and Language Processing, 2013, 21(2): 301-312
[7]	Xiao Y G, Ma L Y, Ward R, Xu L. Fast RLS Fourier analyzers capable of accommodating frequency mismatch. Signal Processing, 2007, 87(4): 2197-2212
[8]	Kuo S M, Puvvala A B. Effects of frequency separation in periodic active noise control systems. IEEE Transactions on Audio, Speech, Language Processing, 2006, 14(5): 1857-1866
[9]	Jeon H J, Chang T G, Kuo S M. Analysis of frequency mismatch in narrowband active noise control. IEEE Transactions on Audio, Speech, Language Processing, 2010, 18(6): 1632-1642
[10]	Dahanayake B W, Upton A R. Derivation of momentum LMS learning algorithms by mimizing objective functions. IEEE International Conference on Neural Networks, 1993, 2(36): 831-835
[11]	Ou Shi-Feng, Gao Ying, Zhao Xiao-Hui. Stochastic gradient based variable momentum factor algorithm for adaptive whitening. Acta Automatica Sinica, 2012, 38(8): 1370-1374 (欧世峰, 高颖, 赵晓晖. 基于随机梯度的变动量因子自适应白化算法. 自动化学报, 2012, 38(8): 1370-1374)
[12]	Dahanayake B W, Upton A R. A novel approach to fast learning: smart neural nets. IEEE World Congress on Computational Intelligence, 1994, 1(5): 572-577
[13]	Ting L K, Cowan C F N, Woods R F. Tracking performance of momentum LMS algorithm for a chirped sinusoidal signal. Processing EUSIPCO, 2000, (3): 985-988
[14]	Chang C Y. Neural filtered-U algorithm for the application of active noise control system with correction terms momentum. Digital Signal Processing, 2010, 20(4): 1019-1026
[15]	Shi Yong, Han Chong-Zhao. Adaptive UKF method with applications to target tracking. Acta Automatica Sinica, 2011, 37(6): 755-759 (石勇, 韩崇昭. 自适应UKF算法在目标跟踪中的应用. 自动化学报, 2011, 37(6): 755-759)
[16]	Qu Cong-Shan, Lu Ting-Zhen, Tan Ying. A modifed empirical mode decomposition method with applications to signal de-noising. Acta Automatica Sinica, 2010, 36(1): 67-73 (曲从善, 路廷镇, 谭营. 一种改进型经验模态分解及其在信号消噪中的应用. 自动化学报, 2010, 36(1): 67-73)
[17]	Cho H, Lee C H, Kim S W. Derivation of a new normalized least mean squares algorithm with modified minimization criterion. Signal Processing, 2009, 89(4): 692-695
[18]	Williamson G A, Clarkson P M, Sethares W A. Performance characteristics of the median LMS adaptive filter. IEEE Transactions on Signal Processing, 1993, 41(2): 1172-1181
[19]	Mathews V J, Xie Z. A stochastic gradient adaptive filter with gradient adaptive step size. IEEE Transactions on Signal Processing, 1993, 41(6): 2075-2087
[20]	Ang W P, Farhang-Boroujeny B. A new class of gradient adaptive step-size LMS algorithms. IEEE Transactions on Circuits and Systems, 2001, 49(4): 805-810
[21]	Chu Zhao-Bi, Zhang Chong-Wei, Feng Xiao-Ying. Adaptive notch filter-based frequency and amplitude estimation. Acta Automatica Sinica, 2010, 36(1): 60-66 (储昭碧, 张崇巍, 冯小英. 基于自适应陷波滤波器的频率和幅值估计. 自动化学报, 2010, 36(1): 60-66)