Active noise control with fast array recursive least squares filters using a parallel implementation for numerical stability
Regular paper
TNO Technical Sciences, Acoustics and Sonar
Wednesday 3 june, 2015, 09:20 - 09:40
0.7 Lisbon (47)
Abstract:
Significant noise reduction in feedforward active noise control systems with a
rapidly changing primary path requires rapid convergence and fast tracking
performance. 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
results of a recent algorithm [1] 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 initialization is enforced, preventing the numerical
instability. The performance of the algorithm is demonstrated in numerical
simulations and in real-time experiments. Convergence rate and tracking
performance are similar to that of a fast-array sliding window recursive least
squares algorithm, while eliminating the numerical 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 filtered reference
least mean squares algorithm.
[1] S. van Ophem and A. P. Berkhoff, A numerically stable, finite memory, fast
array recursive least squares filter for broadband active noise control,
International Journal of Adaptive Control and Signal Processing, 2014,
submitted.