Robust Non-Linear Active Function-Based FxRLS for Impulsive Active Noise Control
Suman Turpati, Ajay Roy, K.C.T. Swamy, Mohammad Khalid Imam Rahmani, Masood Ur Rehman, Sudan Jha, Sultan Ahmad, Jabeen Nazeer, Hikmat A. M. Abdeljaber, Yasser Eid Aljohani
Abstract
Active Noise Control (ANC) is an efficient technique for removing unwanted noise 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., by properly deploying the threshold and changing the error and reference signals using the updating gain of the FxRLS algorithm. The potential of the suggested strategy is proved by simulated results in terms 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.