A covariance matching approach for identifying errors-in-variables systems

  • Authors:

  • Torsten Söderström;Magnus Mossberg;Mei Hong

  • Affiliations:

  • Division of Systems and Control, Department of Information Technology, Uppsala University, P.O. Box 337, SE-75105 Uppsala, Sweden;Department of Physics and Electrical Engineering, Karlstad University, SE-651 88, Karlstad, Sweden;Division of Systems and Control, Department of Information Technology, Uppsala University, P.O. Box 337, SE-75105 Uppsala, Sweden

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2009

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Abstract

The errors-in-variables identification problem concerns dynamic systems whose input and output variables are affected by additive noise. Several estimation methods have been proposed for identifying dynamic errors-in-variables models. In this paper a covariance matching approach is proposed to solve the identification problem. It applies for general types of input signals. The method utilizes a small set of covariances of the measured input-output data. This property applies also for some other methods, such as the Frisch scheme and the bias-eliminating least squares method. Algorithmic details for the proposed method are provided. User choices, for example specification of which input-output covariances to utilize, are discussed in some detail. The method is evaluated by using numerical examples, and is shown to have competitive properties as compared to alternative methods.