Unified approach to adaptive filters and their performance
Unified approach to adaptive filters and their performance
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DOI：
10.1049/iet-spr:20070077
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发表时间：
2008-06
影响因子：
1.7
通讯作者：
J. H. Husøy;M. Abadi
中科院分区：
文献类型：
--
作者：
J. H. Husøy;M. Abadi
提出了一种简化的自适应滤波理论，其中主要的自适应滤波算法可以看作是特例。该理论的算法开发部分包括三个部分：预条件Wiener Hopf方程，通过Richardson迭代得到其最简单的迭代解，以及自相关矩阵、互相关向量和预条件矩阵的估计策略。这导致了一种广义的自适应滤波器，其中直观合理的参数选择给出了主要的自适应滤波算法作为特例。这提供了一种环境，其中许多不同的自适应滤波算法之间的相似和不同被清楚和明确地暴露出来。基于作者提出的广义自适应滤波器，给出了学习曲线、超额均方误差和均方系数偏差的表达式。这些都是通过选择几个参数直接适用于主要自适应滤波算法家族的一般性性能结果。最后，作者通过仿真证明了这些结果在预测自适应滤波性能方面是有用的。
A streamlined theory is presented for adaptive filters within which the major adaptive filter algorithms can be seen as special cases. The algorithm development part of the theory involves three ingredients: a preconditioned Wiener Hopf equation, its simplest possible iterative solution through the Richardson iteration, and an estimation strategy for the autocorrelation matrix, the cross-correlation vector and a preconditioning matrix. This results in a generalised adaptive filter in which intuitively plausible parameter selections give the major adaptive filter algorithms as special cases. This provides a setting where the similarities and differences between the many different adaptive filter algorithms are clearly and explicitly exposed. Based on the authors' generalised adaptive filter, expressions for the learning curve, the excess mean square error and the mean square coefficient deviation are developed. These are general performance results that are directly applicable to the major families of adaptive filter algorithms through the selection of a few parameters. Finally, the authors demonstrate through simulations that these results are useful in predicting adaptive filter performance.