Study of filtered-x logarithmic recursive least $p$-power algorithm

TL;DR

This paper introduces the FxlogRLP algorithm, enhancing impulsive noise control by combining p-order and logarithmic moments, resulting in improved convergence and noise reduction.

Contribution

It proposes the FxlogRLP algorithm, integrating p-order and logarithmic moments for better performance in impulsive noise control.

Findings

FxlogRLP outperforms existing algorithms in convergence speed.

FxlogRLP achieves superior noise reduction.

The algorithm demonstrates robustness to impulsive noise.

Abstract

For active impulsive noise control, a filtered-x recursive least $p$-power (FxRLP) algorithm is proposed by minimizing the weighted summation of the $p$-power of the \emph{a posteriori} errors. Since the characteristic of the target noise is investigated, the FxRLP algorithm achieves good performance and robustness. To obtain a better performance, we develop a filtered-x logarithmic recursive least $p$-power (FxlogRLP) algorithm which integrates the $p$-order moment with the logarithmic-order moment. Simulation results demonstrate that the FxlogRLP algorithm is superior to the existing algorithms in terms of convergence rate and noise reduction.