Design and Analysis of Robust Adaptive Filtering with the Hyperbolic Tangent Exponential Kernel M-Estimator Function for Active Noise Control

TL;DR

This paper introduces a robust adaptive filtering algorithm using a hyperbolic tangent exponential kernel M-estimator for active noise control, effectively handling impulsive noise and outperforming existing methods.

Contribution

The paper develops a novel FXHEKM adaptive algorithm with statistical analysis and demonstrates its superior noise cancellation performance in impulsive noise environments.

Findings

Effective cancellation of impulsive noise with alpha-stable characteristics.

Superior mean-square error and noise reduction compared to competing algorithms.

Computational efficiency validated through analysis.

Abstract

In this work, we propose a robust adaptive filtering approach for active noise control applications in the presence of impulsive noise. In particular, we develop the filtered-x hyperbolic tangent exponential generalized Kernel M-estimate function (FXHEKM) robust adaptive algorithm. A statistical analysis of the proposed FXHEKM algorithm is carried out along with a study of its computational cost. {In order to evaluate the proposed FXHEKM algorithm, the mean-square error (MSE) and the average noise reduction (ANR) performance metrics have been adopted.} Numerical results show the efficiency of the proposed FXHEKM algorithm to cancel the presence of the additive spurious signals, such as \textbf{$\alpha$}-stable noises against competing algorithms.