脉冲噪声的最小均值M-估计有源控制算法

Least Mean M-estimate Algorithm for Active Control of Impulsive Noise

经典的滤波―X最小均方算法(Fx LMS)已经被广泛应用于有源噪声控制(ANC)领域。但是当存在脉冲噪声时,它的性能就会严重退化。基于鲁棒统计的概念介绍了一种新型自适应算法,采用的目标函数为M-估计函数,而不是传统的最小均方误差。该算法分别采用了Huber函数、Hampel三段下降M估计函数等四种不同的M-估计函数作为目标函数,仿真结果表明所采用的算法能有效地消除脉冲噪声,并且与日本学者Akhtar改进的加窗算法相比表现了更好的收敛性。

The classical Filter-x least mean square (Fx LMS) algorithm has been widely used in active noise control (ANC), but its performance would degrade dramatically if there were impulsive noise. In this paper, a new adaptive algorithm based on the concept of robust statistics was presented, whose objective function is the M-estimate function instead of the mean square error (MSE). Four different M-estimate functions, such as Huber function and Hampel’s three parts of re-descending M-estimate function, were used as the objective function and computer simulations were carried out to verify the efficiency of the presented algorithm for active impulsive noise control. The simulation results show that compared with the adding-windows algorithm modified by Japanese scholar Akhtar, the performance of the presented algorithm can eliminate the impulsive noise effectively and has a better convergence.