一种改进的变步长归一化LMS算法

在以往的基于ＬＭＳ算法的研究中，归一化ＬＭＳ算法（ＮＬＭＳ）增大了算法的动态输入范围，但该算法对噪声很敏感。引入自相关的变步长ＬＭＳ算法（ＶＳＳＬＭＳ）不仅加快了收敛速度，可在非平稳状态下进行快速跟踪，而且消除了独立噪声的干扰，但它无法适应大范围的动态输入。本文综合它们的优点而得到的算法，在低信噪比和大范围的动态输入情况下都有良好的性能。

A Refrained Variable Step-size Normazied LMS Algorithm

We have known from the past research based on the LMS algorithm that normazied LMS(NLMS) algorithm enlarges the dynamic range of input, but it is highly sensitive to the noise disturbance. Variable step size LMS algorithm(VSSLMS) which uses auto correlation of errors can provide fast convergence, eliminate the influence of independent noise, thus give good performance in non stationary environment. But it can not perform well with input which has large dynamic range. This paper combinates their advantages, and the new algorithm can perform well both in the environment of low SNR and the environment with large dynamic range of input.