Tracking and convergence of multi-channel kalman filters for active noise control
conference paper
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. Copyright © (2013) by Austrian Noise Abatement Association (OAL).
Organisation: International Institute of Noise Control Engineering (I-INCE)
Organisation: International Institute of Noise Control Engineering (I-INCE)
Topics
TNO Identifier
513299
Publisher
OAL-Osterreichischer Arbeitsring fur Larmbekampfung
Source title
42nd International Congress and Exposition on Noise Control Engineering 2013: Noise Control for Quality of Life, INTER-NOISE 2013, 15-18 September 2013, Innsbruck, Austria
Pages
3445-3454
Files
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