title: Multi-channel Kalman filters for active noise control.
abstract: By formulating the feed-forward broadband active noise control problem as a state estimation problem it is possible 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 derived 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 resulting filter implementation is tested in simulations and in real-time experiments. It was found that for a constant primary path the filter has a fast rate of convergence and is able to track changes in the frequency 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. A forgetting factor lower than 1 gives a significantly improved tracking performance but leads to a numerical instability for the fast array form of the algorithm.
authors.affiliation: University of Twente, Faculty of EEMCS, P.O. Box 217, 7500 AE Enschede, The Netherlands.
authors.forename: S
authors.initials: S
authors.lastname: van Ophem
authors.forename: A P
authors.initials: AP
authors.lastname: Berkhoff
mesh_terms: D000162:Acoustics / Q000295:instrumentation; D000465:Algorithms; D003198:Computer Simulation; D004301:Doppler Effect; D016018:Least-Squares Analysis; D009038:Motion; D009624:Noise, Transportation; D011312:Pressure; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013016:Sound; D013018:Sound Spectrography; D013997:Time Factors