The Development of Median and Order Statistic Adaptive Filters

本建议涉及一个关于发展和分析 一类鲁棒的自适应滤波算法，可以提供 在冲动和其他非冲动的环境中， 高斯噪声 这些滤波器被称为顺序统计最小 均方（OSLMS）自适应滤波器。 这类的一个特例 是中值LMS（MLMS）算法，源自熟悉的LMS 算法通过替换的瞬时测量 均方误差性能曲面的梯度， LMS滤波器，用样本的中值表示量。

This proposal is concerned with the development and analysis of a class of robust adaptive filtering algorithms which can offer effective performance in an environment of impulsive and other non- gaussian noise. These filters are termed Order Statistic Least Mean Square (OSLMS) adaptive filters. A special case of this class is the Median LMS (MLMS) algorithm, derived from the familiar LMS algorithm by replacing the instantaneous measurements of the gradient of the mean square error performance surface, used by the LMS filter, with the sample median of that quantity.