Robust Non-Linear Active Function-Based FxRLS for Impulsive Active Noise Control

Abstract

Active Noise Control (ANC) is an effective technique for removing undesirable disturbances based on destructive interference between two noises (i.e., the superposition principle). To reduce the Non-Gaussian distribution of impulsive noises, the ANC is implemented using a prominent Filtered Cross Least Mean Square (FxLMS) method that relies on reducing the unwanted noise. The standard FxLMS method fails to adapt to its specifications, resulting in poor convergence and instability in the presence of impulsive noises and a non-linear response from the ANC system’s components. The Least Square family of Recursive Least Square (RLS) increases ANC performance by offering superior convergence performance to traditional stochastic algorithms. This paper proposes a novel technique called the recursive non-linear active threshold-based and modified gain FXRLS (NAMGFxRLS) algorithm to overcome the inadequacies of impulsive noise and non-linearity issues in the ANC. The suggested technique aims to automatically modify weights by the various sample processes, i.e., employing the FxRLS algorithm’s updated gain to adjust the error and reference signals and appropriately deploying the threshold. The potential of the suggested strategy is proved by simulated results of convergence speed, stability, and excellent Mean Noise Reduction of roughly 52.8 % for various noises when compared to previous approaches, notably large impulsive noises.

Keywords

• Active noise control
• active functions
• FxLMS
• symmetric α stable distributions
• recursive least square