An improved bias-compensation approach for errors-in-variables model identification

Authors:
Kaushik Mahata
Affiliations:
School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia
Venue:
Automatica (Journal of IFAC)
Year:
2007

Citing 12
Cited 3

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Abstract

Parametric estimation of the dynamic errors-in-variables models is considered in this paper. In particular, a bias compensation approach is examined in a generalized framework. Sufficient conditions for uniqueness of the identified model are presented. Subsequently, a statistical accuracy analysis of the estimation algorithm is carried out. The asymptotic covariance matrix of the system parameter estimates depends on a user chosen filter and a certain weighting matrix. It is shown how these can be tuned to boost the estimation performance. The numerical simulation results suggest that the covariance matrix of the estimated parameter vector is very close to the Cramer-Rao lower bound for the estimation problem.