Paper Info
Title
A Kalman filter approach to active noise control
Abstract
Most Active Noise Control (ANC) systems use some form of the LMS [5] [9] algorithm due to its reduced computational complexity. However, the problems associated with it are well-known, namely slow convergence and high sensitivity to the eigenvalue spread [3] [9]. To overcome these problems the RLS algorithm is often used, but it is now widely known, that the RLS loses many of its good properties for a forgetting factor lower than one. Namely, it has been shown that in some applications the LMS algorithm is actually better in tracking non-stationary systems than the RLS algorithm [2] [3]. One approach, which works well with non-stationary systems, is to use some specialized form of the Kalman filter, which can be interpreted as a generalization of the RLS algorithm [1][3][4]. The Kalman filter has a high computational complexity, similar to that of the RLS algorithm, which can make it costly for some applications. Nevertheless, for narrow-band ANC, the number of taps is not very large [9], and the application of the Kalman filter in ANC may be easily handled by today DSP's. In this paper, a specialized version of the Kalman filter fitted to ANC is developed; both control filter adaptation and secondary path modeling. It is shown, throw computer experiments, that a large reduction in the residual noise can be achieved in non-stationary environments, compared with the LMS and RLS based algorithms, especially when on-line secondary path modeling is used.
Year
Venue
Keywords
2000
EUSIPCO
noise,kalman filters,mathematical model
Field
DocType
ISBN
Least mean squares filter,Noise reduction,Computer experiment,Signal processing,Control theory,Kalman filter,Active noise control,Recursive least squares filter,Mathematics,Computational complexity theory
Conference
978-952-1504-43-3
Citations 
PageRank 
References 
16
1.92
2
Authors
2
Name
Order
Citations
PageRank
P. A. C. Lopes1313.90
Moisés Simões Piedade24314.31