Implementation of Kalman Filter Approach for Active Noise Control by Using MATLAB: Dynamic Noise Cancellation

TL;DR

This paper demonstrates that using a Kalman filter in active noise control systems improves dynamic noise reduction and convergence speed compared to traditional FxLMS algorithms, with implementation details provided in MATLAB.

Contribution

It introduces a novel Kalman filter-based model for active noise control, outperforming conventional adaptive filters in dynamic noise environments.

Findings

Kalman filter shows superior convergence in dynamic noise scenarios.

The proposed model enhances noise reduction effectiveness.

Implementation code is available online.

Abstract

This article offers an elaborate description of a Kalman filter code employed in the active control system. Conventional active noise management methods usually employ an adaptive filter, such as the filtered reference least mean square (FxLMS) algorithm, to adjust to changes in the primary noise and acoustic environment. Nevertheless, the slow convergence characteristics of the FxLMS algorithm typically impact the effectiveness of reducing dynamic noise. Hence, this study suggests employing the Kalman filter in the active noise control (ANC) system to enhance the efficacy of noise reduction for dynamic noise. The ANC application effectively utilizes the Kalman filter with a novel dynamic ANC model. The numerical simulation revealed that the proposed Kalman filter exhibits superior convergence performance compared to the FxLMS algorithm for handling dynamic noise. The code is available on \href{https://github.com/ShiDongyuan/Kalman_Filter_for_ANC.git}{GitHub} and \href{https://www.mathworks.com/matlabcentral/fileexchange/159311-kalman-filter-for-active-noise-control}{MathWorks}.