Active impulsive noise control algorithm based on an arctangent function

Abstract

To address the issue that the filtered-x least mean square (FxLMS) algorithm loses its stability in impulsive noise environments resulting in failure of noise reduction, a modified reference signal filtered-x arctangent least mean square (MRFxatanLMS) algorithm is proposed. A nonlinear function is utilized to constrain abnormally large values in the reference signal without requiring complex threshold estimation. By embedding the cost function of the traditional FxLMS algorithm into the framework of the arctangent function, a novel cost function is formed to control the impulsive noise and improve the robustness of the noise reduction system. A compression factor is introduced into the cost function to adjust the compression degree of the error signal, thereby balancing the convergence speed and robustness of the algorithm. Simulation results demonstrate that compared with several other algorithms, the proposed algorithm performs well in different intensities of impulsive noise environments.