Unifying some higher-order statistic-based methods for errors-in-variables model identification

Stéphane Thil (1) , Wei Xing Zheng (2) , Marion Gilson (3) , Hugues Garnier (3)

1 ELIAUS - Electronique, Informatique, Automatique et Systèmes
2 SCEM - School of Computing, Engineering and Mathematics [Sydney]
3 CRAN - Centre de Recherche en Automatique de Nancy

In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical example is presented which confirms the theoretical results. Some possible extensions of the methods are then given.

Mots clés: System identification; errors-in-variables; instrumental variable; higher-order statistics

Domaines: Automatique / Robotique

Stéphane Thil, Wei Xing Zheng, Marion Gilson, Hugues Garnier. Unifying some higher-order statistic-based methods for errors-in-variables model identification. Automatica, 2009, 45 (8), pp.1937-1942. ⟨10.1016/j.automatica.2009.04.019⟩. ⟨hal-00406686⟩