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

Authors:
Stéphane Thil;Wei Xing Zheng;Marion Gilson;Hugues Garnier
Affiliations:
Laboratoire ELIAUS, Université de Perpignan Via Domitia, 52 avenue Paul Alduy, 66860 Perpignan, France;School of Computing and Mathematics, University of Western Sydney, Penrith South DC NSW 1797, Australia;Centre de Recherche en Automatique de Nancy (CRAN UMR 7039), Nancy-Université, CNRS, BP 239, 54506 Vanduvre-lès-Nancy Cedex, France;Centre de Recherche en Automatique de Nancy (CRAN UMR 7039), Nancy-Université, CNRS, BP 239, 54506 Vanduvre-lès-Nancy Cedex, France
Venue:
Automatica (Journal of IFAC)
Year:
2009

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Abstract

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.