Noise control with Kalman filter for active headrest

A control strategy with Kalman filter (KF) is proposed for active noise control of virtual error signal for active headset. Comparing with the gradient based algorithm, KF algorithm has faster convergence speed and better convergence performance. In this paper, the state equation of the system is es...

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Main Authors: WANG Lei, CHEN Kean, XU Jian, QI Wang
Format: Article
Language:zho
Published: EDP Sciences 2021-10-01
Series:Xibei Gongye Daxue Xuebao
Subjects:
Online Access:https://www.jnwpu.org/articles/jnwpu/full_html/2021/05/jnwpu2021395p937/jnwpu2021395p937.html
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author WANG Lei
CHEN Kean
XU Jian
QI Wang
author_facet WANG Lei
CHEN Kean
XU Jian
QI Wang
author_sort WANG Lei
collection DOAJ
description A control strategy with Kalman filter (KF) is proposed for active noise control of virtual error signal for active headset. Comparing with the gradient based algorithm, KF algorithm has faster convergence speed and better convergence performance. In this paper, the state equation of the system is established on the basis of virtual error sensing, and only the weight coefficients of the control filter are considered in the state variables. In order to ensure the convergence performance of the algorithm, an online updating strategy of KF parameters is proposed. The fast-array method is also introduced into the algorithm to reduce the computation. The simulation results show that the present strategy can improve the convergence speed and effectively reduce the noise signal at the virtual error point.
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spelling doaj.art-70ed3b033e8b4493a5def1d73f6025232023-10-02T09:44:28ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252021-10-0139593794410.1051/jnwpu/20213950937jnwpu2021395p937Noise control with Kalman filter for active headrestWANG Lei0CHEN Kean1XU Jian2QI Wang3School of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversityA control strategy with Kalman filter (KF) is proposed for active noise control of virtual error signal for active headset. Comparing with the gradient based algorithm, KF algorithm has faster convergence speed and better convergence performance. In this paper, the state equation of the system is established on the basis of virtual error sensing, and only the weight coefficients of the control filter are considered in the state variables. In order to ensure the convergence performance of the algorithm, an online updating strategy of KF parameters is proposed. The fast-array method is also introduced into the algorithm to reduce the computation. The simulation results show that the present strategy can improve the convergence speed and effectively reduce the noise signal at the virtual error point.https://www.jnwpu.org/articles/jnwpu/full_html/2021/05/jnwpu2021395p937/jnwpu2021395p937.htmlactive noise controlkalman filteractive headset
spellingShingle WANG Lei
CHEN Kean
XU Jian
QI Wang
Noise control with Kalman filter for active headrest
Xibei Gongye Daxue Xuebao
active noise control
kalman filter
active headset
title Noise control with Kalman filter for active headrest
title_full Noise control with Kalman filter for active headrest
title_fullStr Noise control with Kalman filter for active headrest
title_full_unstemmed Noise control with Kalman filter for active headrest
title_short Noise control with Kalman filter for active headrest
title_sort noise control with kalman filter for active headrest
topic active noise control
kalman filter
active headset
url https://www.jnwpu.org/articles/jnwpu/full_html/2021/05/jnwpu2021395p937/jnwpu2021395p937.html
work_keys_str_mv AT wanglei noisecontrolwithkalmanfilterforactiveheadrest
AT chenkean noisecontrolwithkalmanfilterforactiveheadrest
AT xujian noisecontrolwithkalmanfilterforactiveheadrest
AT qiwang noisecontrolwithkalmanfilterforactiveheadrest