非线性主动噪声控制的随机傅里叶特征-核滤波算法

传统的线性主动噪声控制算法在噪声信号或噪声通道呈现非线性特性的情况下控制效果不佳。核-滤波最小均方误差算法(Kernel Filtered x Least Mean Square,KFxLMS)通过将输入噪声信号映射到高维再生核希尔伯特空间,再用线性方法在高维空间中进行处理。然而,随着新噪声信号的输入,KFxLMS算法递增的核函数运算需要较高的成本。为进一步改进KFxLMS算法,本文提出了随机傅里叶特征核滤波最小均方误差算法(Random Fourier Feature-Kernel Filtered x Least Mean Square,RFF-KFxLMS)。在仿真实验部分讨论了算法的参数选择,给出了算法的计算耗时,并验证了提出的RFF-KFxLMS算法在非线性噪声通道情况下,针对不同频率分量的正弦噪声都能够达到理想的性能。

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.