Robust Q-Gradient Subband Adaptive Filter for Nonlinear Active Noise Control

Abstract

Active noise control (ANC) is gaining attention for attenuating noise from a remote location. Considering the problem of nonlinear active noise control (NLANC) at a virtual location, a robust filtered-s subband adaptive filtering algorithm based on the $q$ -gradient maximum correntropy criterion (RFsSAF-qMCC) is proposed in this paper. The proposed RFsSAF-qMCC algorithm develops the functional link artificial neural network (FLANN)-SAF structure as the controller, and embeds the MCC with the concept of q -gradient, thereby improving the convergence speed in the impulsive environment. To solve the trade-off between fast convergence and low noise residue caused by the fixed q -gradient, a variable q -gradient algorithm, termed as RFsSAF-vqMCC, is further developed. As an additional contribution, the convergence behavior of the proposed RFsSAF-qMCC and RFsSAF-vqMCC algorithms is analyzed. Simulation results corroborate the effectiveness of the proposed algorithms as compared to state-of-the-art algorithms.