The feed-forward broadband active noise control problem can be formulated as a state estimation problem to achieve a faster rate of convergence than the filtered reference least mean squares algorithm and possibly also a better tracking performance. A multiple input/multiple output Kalman algorithm is used to perform this state estimation. To make the algorithm more suitable for real-time applications the Kalman filter is written in a fast array form and the secondary path state matrices are implemented in output normal form. The implementation was tested in simulations and in real-time experiments. It was found that for a constant primary path the Kalman filter has a fast rate of convergence and is able to track changes in the spectrum. For a forgetting factor equal to unity the system is robust, but the filter is unable to track rapid changes in the primary path. It is shown that a forgetting factor lower than unity gives a significantly improved tracking performance. Numerical issues of the fast array form of the algorithm for such forgetting factors are discussed and possible solutions are presented.
|Title of host publication||Proceedings Internoise 2013|
|Place of Publication||Innsbruck|
|Publication status||Published - 15 Sep 2013|
|Event||42nd International Congress and Exposition on Noise Control Engineering, INTERNOISE 2013: Noise Control for Quality of Life - Innsbruck, Austria|
Duration: 15 Sep 2013 → 18 Sep 2013
Conference number: 42
|Name||paper no 0274|
|Conference||42nd International Congress and Exposition on Noise Control Engineering, INTERNOISE 2013|
|Abbreviated title||INTERNOISE 2013|
|Period||15/09/13 → 18/09/13|
Berkhoff, A. P., & van Ophem, S. (2013). Tracking and convergence of multi-channel Kalman filters for active noise control. In Proceedings Internoise 2013 (pp. 1-10). (paper no 0274). Innsbruck: INCE.