A method on parameter estimation of nonlinear systems

Author

Horio, Makoto ; Moriomto, Jiro ; Tabuchi, Toshiaki

Author_Institution

Grad. Sch. of Eng., Tokushima Bunri Univ., Sanuki

fYear

2008

fDate

20-22 Aug. 2008

Firstpage

1040

Lastpage

1043

Abstract

It is developed that the state estimation of nonlinear system model based on maximum a posteriori (MAP) estimation. This is equivalent to the minimization problem of the object function derived from a posteriori probability density function.It is shown that the minimizer based on Newton-method becomes to iterated extended Kalman filter (IEKF). A method on the gain adjustment of the MAP estimator is given so as to improve the performance of it. Then a method of the parameter setting for the gain adjustment is given.

Keywords

Kalman filters; Newton method; maximum likelihood estimation; minimisation; nonlinear systems; probability; state estimation; MAP estimator; Newton-method; gain adjustment; iterated extended Kalman filter; maximum a posteriori estimation; minimization problem; nonlinear system model; nonlinear systems; parameter estimation; probability density function; state estimation; Equations; Gain measurement; Kalman filters; Minimization methods; Nonlinear dynamical systems; Nonlinear systems; Parameter estimation; Performance gain; State estimation; Time measurement; MAP estimation; convergence; gain adjustment; nonlinear system; parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual Conference, 2008

Conference_Location

Tokyo

Print_ISBN

978-4-907764-30-2

Electronic_ISBN

978-4-907764-29-6

Type

conf

DOI

10.1109/SICE.2008.4654809

Filename

4654809