Parameter identification for state-space models with nuisance parameters

Author

Spall, James C. ; Garner, John P.

Author_Institution

Johns Hopkins Univ., Laurel, MD, USA

Volume

26

Issue

6

fYear

1990

fDate

11/1/1990 12:00:00 AM

Firstpage

992

Lastpage

998

Abstract

The problem of identifying parameters in a dynamic model is considered based on the premise that certain parameters not being estimated are not known precisely. A procedure is described for accounting for these imprecisely known nuisance parameters when estimating the primary parameters of interest. The technique uses the asymptotic normality of the estimate together with the implicit function theorem to determine a correction to the estimate uncertainty evaluated from the Fisher information matrix. Efficient evaluation of the correction using Kalman filters is discussed and a numerical example for the X-22A aircraft is presented

Keywords

Kalman filters; aircraft control; parameter estimation; state-space methods; Fisher information matrix; Kalman filters; X-22A aircraft; asymptotic normality; dynamic model; nuisance parameters; parameter identification; primary parameters; state-space models; Covariance matrix; Laboratories; Maximum likelihood estimation; Military aircraft; Parameter estimation; Physics; Springs; State estimation; System testing; Uncertainty;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.62251

Filename

62251