System Identification with Perfect Sequence Excitation - Efficient NLMS vs. Inverse Cyclic Convolution
System Identification with Perfect Sequence Excitation - Efficient NLMS vs. Inverse Cyclic Convolution
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具有完美序列激励的系统辨识 - 高效 NLMS 与逆循环卷积
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Vary
中科院分区：
文献类型：
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作者：
C. Antweiler;Stefan Kühl;B. Sauert;P. Vary
线性传输系统通常以其脉冲响应为特征。一个简单而快速的方法来获取这些脉冲响应是归一化最小均方（NLMS）算法与完美的序列激励相结合。它不仅适用于静态脉冲响应测量，而且特别适合于时变线性系统的跟踪。本文讨论了完美序列激励的NLMS算法的不同实现策略，即高效NLMS算法和逆循环卷积算法，并从性能、复杂度和适用性方面进行了比较。作为一个主要的结果，它表明，在某些条件下，所有的算法变量可以转移到对方。
Linear transmission systems are often characterized by their impulse responses. A simple and fast approach to acquire these impulse responses is the normalized leastmean-square (NLMS) algorithm in combination with a perfect sequence excitation. It is not only applicable to static impulse response measurements, but has been optimized especially for the tracking of time varying linear systems. In this paper, different implementation strategies of the perfect sequence excited NLMS algorithm, namely the efficient NLMS and the inverse cyclic convolution algorithm, are discussed and compared in terms of performance, complexity, and applicability. As a main result it is shown that for certain conditions all algorithmic variants can be transferred to each other.