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

• Published in: Automatica (Oxford) Vol. 43; no. 8; pp. 1339 - 1354
• Main Author: Mahata, Kaushik
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-08-2007; Elsevier
• Subjects: Applied sciences; Computer science; control theory; systems; Continuous-time models; Control theory. Systems; Errors-in-variables; Exact sciences and technology; Linear estimation; Modelling and identification; System identification; System identification; Linear estimation; Continuous-time models; Errors-in-variables; Input error; Estimation error; Output error; Statistical analysis; Error estimation; Bias; Continuous time; Covariance matrix; Modeling; Model matching; Filter; Asymptotic approximation; Error correction; Dynamic model; Algorithm analysis; Cramer Rao inequality
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2007.01.011

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 Cramér–Rao lower bound for the estimation problem.