An Error Innovation-based Adaptive Kalman Filter for multi-channel Active Noise Control

This paper presents an Error Innovation-based Adaptive Estimation Kalman Filter (eIAEKF) tailored for multi-channel Active Noise Control (ANC) systems using MIMO filtered-x structures. Conventional Kalman-based ANC methods are computationally demanding and typically rely on fixed or manually tuned noise covariance matrices, limiting their robustness under changing acoustic conditions. The proposed eIAEKF introduces an error-driven mechanism that adaptively updates the measurement-noise covariance R ( n ) using the measured error signal, directly aligning the estimation process with the ANC objective. Building on the principles of innovation-based adaptive estimation, the algorithm reduces computational complexity by avoiding the need for an explicit transition matrix and by simplifying the computation of the Kalman gain, while also improving robustness to poor initialization. Simulation results under both stationary and dynamic scenarios, including changes in the primary noise spectrum, source position, and microphone location, show that eIAEKF achieves faster convergence and higher attenuation than an adaptive Kalman-like baseline, while maintaining numerical stability. Real-time experiments on a hardware platform further demonstrate the practical viability of the proposed approach. • The effectiveness of the Kalman filter depends on the elusive initial knowledge. • The noise covariance matrices control the responsiveness of the filter. • Traditional Kalman Filter keeps the parameters time-invariant. • Adaptive parameters can make the filter suitable for real-world applications.