Sigmoid函数变步长LMS自适应算法的抗干扰性能改进

为了改善Sigmoid函数变步长LMS算法（SVS-LMS）在高斯噪声和冲激噪声干扰下的性能，首先将以瞬间误差功率为Sigmoid函数自变量控制步长更新的方法，改为以误差的自相关时间均值估计调节步长，抑制了噪声干扰；然后使用HB加权进一步平滑了因噪声干扰导致的自适应滤波器权系数伪峰、使用归一化处理获得了更大的输入信号动态范围。自适应时延估计仿真实验表明，在高斯噪声和冲激噪声干扰下，相比于固定参数下的SVS-LMS算法和另外一种SVS-LMS改进算法，本文算法及其HB加权能够获得更好的时变时延跟踪均方误差性能。

Anti-Interference Performance Improvement for Sigmoid Function Variable Step-Size LMS Adaptive Algorithm

In order to improve the performance of variable step-size LMS-type algorithm based on Sigmoid function (SVS-LMS) for case of Gaussian noise and impulsive noise interference,firstly, the method of updating step-size controlled by instantaneous error’s power is replaced by adjusting step-size under the control of time-averaged estimate of error autocorrelation. Then, HB weighted method is introduced into the algorithm to restrain fake peaks of adaptive filter’s coefficient vector and normalized process is introduced to obtain a greater dynamic range of input signal. Simulation results of tracking time-varying delay show that, comparing to SVS-LMS and another refrained SVS-LMS algorithm being of fixed parameters, the algorithm and its HB weighted method can achieve superior MSE performance.