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Frequency domain identification of FIR models in the presence of additive input–output noise
Automatica ( IF 6.4 ) Pub Date : 2020-02-13 , DOI: 10.1016/j.automatica.2020.108879
Umberto Soverini , Torsten Söderström

This paper describes a new approach for identifying FIR models from a finite number of measurements, in the presence of additive and uncorrelated white noise. In particular, two different frequency domain algorithms are proposed. The first algorithm is based on some theoretical results concerning the dynamic Frisch scheme. The second algorithm maps the FIR identification problem into a quadratic eigenvalue problem. Both methods resemble in many aspects some other identification algorithms, originally developed in the time domain. The features of the proposed methods are compared with each other and with those of some time domain algorithms by means of Monte Carlo simulations.



中文翻译:

存在加性输入输出噪声的FIR模型的频域识别

本文介绍了一种在存在加性和不相关白噪声的情况下从有限数量的测量中识别FIR模型的新方法。特别地,提出了两种不同的频域算法。第一种算法基于有关动态Frisch方案的一些理论结果。第二种算法将FIR识别问题映射为二次特征值问题。两种方法在很多方面都与最初在时域中开发的某些其他识别算法相似。通过蒙特卡洛模拟,将所提出的方法的特征相互之间以及与某些时域算法的特征进行了比较。

更新日期:2020-02-13
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