Adaptive fir filters with automatic length optimization by monitoring a normalized combination scheme
Adaptive fir filters with automatic length optimization by monitoring a normalized combination scheme
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通过监控归一化组合方案实现自动长度优化的自适应冷杉滤波器
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Walter Kellermann
中科院分区：
文献类型：
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作者：
M. Zeller;L. A. Azpicueta;Walter Kellermann
本文提出了一种新的自适应滤波策略，它提供了一种根据记忆长度自动自配置滤波器结构的策略。通过监视具有不同系数数目的两个竞争滤波器的归一化组合的自适应混合，获得最佳滤波器长度的在线估计，并使用该估计来动态缩放所采用的滤波器的大小。此外，还提出了这一方法的一个更有效、更简化的版本，并证明它在显著降低所需复杂性的同时同样有效。对高阶真实系统以及平稳噪声和语音信号的实验结果表明，所提出的算法在实际系统辨识场景中具有良好的性能和稳健的跟踪性能。
This paper presents a novel strategy of adaptive filtering which provides an automatic self-configuration of the filter structure in terms of memory length. By monitoring the adaptive mixing of a normalized combination of two competing filters with a different number of coefficients, an online estimate of the optimum filter length is obtained and used to dynamically scale the size of the employed filters. Furthermore, a more efficient, simplified version of this approach is proposed and shown to be equally effective while significantly reducing the required complexity. Experimental results for high-order real-world systems as well as stationary noise and speech signals demonstrate the good performance and the robust tracking behaviour of the outlined algorithms in the context of realistic system identification scenarios.