The Development of Median and Order Statistic Adaptive Filters

本文研究了一类鲁棒自适应滤波算法的发展和分析，该算法能够在脉冲和其他非高斯噪声环境中提供有效的性能。这些滤波器被称为序统计最小均方（OSLMS）自适应滤波器。这类算法的一个特殊情况是中值LMS （MLMS）算法，它源自于我们熟悉的LMS算法，通过将LMS滤波器使用的均方误差性能面梯度的瞬时测量值替换为该量的样本中值。

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.