基于噪音拟合的优化变步长滤波最小均方算法

Optimal variable step filtered-x least mean square algorithm based on noise fitting

为了更快地实现主动降噪,设计了噪音多项式拟合模型,提出了改进的变步长滤波最小均方算法(Improved Filtered-x Least Mean Square,IFxLMS)。该算法在统计噪音信号的同时,对噪音信号进行拟合与预测,随后结合误差信号与预测信号对步长进行调节,达到快速调节的目的。为了验证该算法的性能,将该算法与传统变步长滤波最小均方算法对比试验,仿真结果显示,在相同噪音条件下,新算法将噪音信号降到10 dB、20 dB、30 dB、35 dB等信噪比时,所需的迭代次数减少了4次~60次不等,在同时新算法的鲁棒性也优于普通的滤波变步长最小均方算法。

In order to achieve active noise reduction faster,a noise polynomial fitting model is designed,and an improved variable step size filtering least mean square algorithm(improved filtered-x least mean square,IFxLMS)is proposed.The algorithm performs fitting and prediction to the noise signal while counting the noise signal,and then adjusts the step length by combining the error signal and the predicted signal to achieve the purpose of rapid adjustment.In order to verify the performance of the algorithm,the algorithm is compared with the traditional variable step filter-x least mean square algorithm.The simulation results show that under the same noise conditions,when the new algorithm reduces the noise signal to 10 dB,20 dB,30 dB,35 dB,etc.The number of iterations required has been reduced from 4 to 60.At the same time,the robustness of the new algorithm is better than that of the ordinary variable step size filtered-x least mean square algorithm.