Noise control with Kalman filter for active headrest

应用卡尔曼滤波的有源头靠噪声控制策略

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Received: 12 January 2021

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.

摘要

针对有源头靠中虚拟误差点噪声信号的自适应控制问题，提出了一种应用卡尔曼滤波（KF）的控制策略。与基于梯度的算法相比，KF具有更快的收敛速度和更好的收敛性能。在虚拟误差传感的基础上建立了系统的状态方程，在状态变量中仅考虑控制滤波器权系数。为了保证算法收敛，给出了KF参数的在线更新策略，同时在算法中引入快速阵列方法从而降低运算量。仿真结果表明，文中提出的策略能够有效提升系统的收敛速度，降低虚拟误差点处的噪声信号。