Active noise control with fast array recursive least squares filters using a parallel implementation for numerical stability
conference paper
Noise reduction in feedforward active noise control systems with a rapidly changing primary path requires rapid convergence and fast tracking. This can be accomplished with a fast-array Kalman method which uses an efficient rotation matrix technique to calculate the filter parameters. However, finite precision effects lead to unstable behavior. In this paper resuits of a recent algorithm are presented, which exhibits the fast convergence, tracking properties and the linear calculation complexity of the fast array Kalman method, but which does not suffer from the numerical problems. This is achieved by using a convex combination of two parallel finite length growing memory recursive least squares filters. A periodic reset of the filter parameters with proper re-initialization is enforced, preventing the numerical instability. The performance of the algorithm is demonstrated in numencal simulations and in real-time experiments. Convergence rate and tracking performance are similar to that of a fast-array sliding window recursîve least squares algorithm, while eliminating the numencal issues. It is shown that the new algorithm provides significantly improved convergence and tracking as compared to more traditional algorithms, such as based on the fihered reference least mean squares algorithm
Topics
TNO Identifier
526293
Publisher
European Acoustical association
Source title
Proceedings 10th European Congress and Exposition on Noise Control Engineering, EuroNoise2015, 31 May - 3 June, Maastricht, The Netherlands
Pages
2183-2188