Impulsive noise is widely distributed in various scenarios and becomes an important challenge for the practical applications of active noise control (ANC) system. The conventional ANC algorithms based on the transformation function have a fixed compression level for error signal, leading to slow convergence and weak noise reduction under certain circumstances. To overcome this defect, this paper proposes an enhanced filtered-x arctangent error Least Mean Square (EFxatanLMS) algorithm by designing an adjustable nonlinear transformation function of error signal with arctangent form. Specifically, a compression factor is introduced in the transformation function to govern the compression shape of the function so as to realize ideal effect on impulsive noise with different intensities. For the purpose of further optimizing the capability of the proposed algorithm, an improved normalized step-size EFxatanLMS (NSS-EFxatanLMS) algorithm is proposed. It adopts a novel time-varying normalized function to adjust the step-size coefficient to the appropriate value adaptively. Numerical simulations verify the effectiveness of the proposed algorithms for Gaussian noise and non-Gaussian impulsive noise.

脉冲噪声广泛分布在各种情况下，并成为主动噪声控制（ANC）系统的实际应用中的重要挑战。基于变换函数的常规ANC算法对错误信号具有固定的压缩级别，从而在某些情况下会导致收敛缓慢和噪声降低。为克服此缺陷，本文通过设计具有反正切形式的误差信号的可调非线性变换函数，提出了一种增强的滤波x反正切误差最小均方（EFxatanLMS）算法。具体地，在变换函数中引入压缩因子以控制函数的压缩形状，从而实现对不同强度的脉冲噪声的理想效果。为了进一步优化所提出算法的性能，提出了一种改进的归一化步长EFxatanLMS（NSS-EFxatanLMS）算法。它采用了新颖的时变归一化函数，可将步长系数自适应地调整到适当的值。数值仿真验证了所提算法对高斯噪声和非高斯脉冲噪声的有效性。