Noise control with Kalman filter for active headrest

Lei Wang,Wang Qi,Jian Xu,Kean Chen

doi:10.1051/jnwpu/20213950937

Abstract

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.

Highlights

Multi⁃channel Kalman filters for active noise control[ J]
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
The fast⁃ array method is introduced into the algorithm to reduce the computation

Summary

Introduction

区别于传统的无源噪声控制, 有源噪声控制 ( active noise control,ANC) 利用声波的相消性干涉, 主要在低频范围发挥作用[1] 。一般情况下,人们采用局部空间降噪代替全局降噪以降低系统复杂度, 这样只需要在人耳附近获得降噪效果即可,因此有源头靠技术有着重要的实际价值[2] 。通过虚拟误差传感技术将局部静区从物理误差传声器位置处移至虚拟误差点位置。为了实现静区的传递,采用 RMT 的 ANC 系统如图 2 所示。前文中一致。 inv(Kv × Kv) 表示对 Kv × Kv 阶矩阵求逆所需的运算量。 (12) ~ (14) 式中 P( n) rv( n) 的值可以重复使用,并且由于 P(n) 为埃尔米特矩阵, 有( P( n) rv( n) ) T = rTv P( n) 。由(16) 式可以看出, KF 算法的运算复杂度较高。由(14) 式可得 Q1( n) = diag{ E[ Kk( n) R -1( n) α( n) ] · [ Kk( n) R -1( n) α( n) ] T}

Results

Conclusion