This paper provides an analysis of transient and steady-state behavior of different filtered-x affine projection algorithms. Algorithms suitable for single-channel and for multichannel active noise controllers are treated within a unified framework. Very mild assumptions are posed on the active noise control system model, which is only required to have a linear dependence of the output from the filter coefficients. Therefore, the analysis applies not only to the linear finite impulse response models but also to nonlinear Volterra filters, i.e., polynomial filters, and other nonlinear filter structures. The convergence analysis presented in this paper relies on energy conservation arguments and does not apply the independence theory, nor does it impose any restriction to the signal distributions. It is shown in the paper that filtered-x affine projection algorithms always provide a biased estimate of the minimum mean square solution. Nevertheless, in many cases, the bias is small and therefore these algorithms can be profitably applied to active noise control.
Transient and steady-state analysis of filtered-X affine projection algorithms
CARINI, ALBERTO;
2006
Abstract
This paper provides an analysis of transient and steady-state behavior of different filtered-x affine projection algorithms. Algorithms suitable for single-channel and for multichannel active noise controllers are treated within a unified framework. Very mild assumptions are posed on the active noise control system model, which is only required to have a linear dependence of the output from the filter coefficients. Therefore, the analysis applies not only to the linear finite impulse response models but also to nonlinear Volterra filters, i.e., polynomial filters, and other nonlinear filter structures. The convergence analysis presented in this paper relies on energy conservation arguments and does not apply the independence theory, nor does it impose any restriction to the signal distributions. It is shown in the paper that filtered-x affine projection algorithms always provide a biased estimate of the minimum mean square solution. Nevertheless, in many cases, the bias is small and therefore these algorithms can be profitably applied to active noise control.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.