Title :
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
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;
Conference_Titel :
SICE Annual Conference, 2008
Conference_Location :
Tokyo
Print_ISBN :
978-4-907764-30-2
Electronic_ISBN :
978-4-907764-29-6
DOI :
10.1109/SICE.2008.4654809
