Design and Analysis of Robust Adaptive Filtering with the Hyperbolic Tangent Exponential Kernel M-Estimator Function for Active Noise Control
This work addresses noise cancellation challenges in practical applications like audio systems or industrial settings where impulsive noise is problematic, representing an incremental improvement over existing methods.
The authors tackled the problem of active noise control in impulsive noise environments by proposing the FXHEKM robust adaptive filtering algorithm, which achieved improved noise cancellation performance against competing algorithms as measured by mean-square error and average noise reduction metrics.
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{$α$}-stable noises against competing algorithms.