Random Fourier Feature Kernel Filtered-x LMS Algorithm for Nonlinear Active Noise Control System

The performance of the traditional linear active noise control algorithms degrades when the noise signal or primary path is nonlinear. The Kernel Filtered x Least Mean Square (KFxLMS) algorithm maps the input noise signal to the higher-dimensional reproducing kernel Hilbert space, and then adopts the linear method to process the mapped signal. However, with the feed of new noise signal, the KFxLMS algorithm requires a high cost to realize the kernel calculation. In this paper, a nonlinear active noise control algorithm Random Fourier Feature - Kernel Filtered x Least Mean Square (RFF-KFxLMS) algorithm is proposed. In the simulation experiment, the parameter selection is discussed, and the consuming time of the algorithm is given. In the case of nonlinear primary path, the proposed RFF-KFxLMS algorithm is verified by comparative experiments to achieve ideal performance in condition of sinusoidal noises with different frequency components.