Recursive state estimators for general discrete-time stochastic nonlinear models

Author

Yaz, Edwin E. ; Bari, Mohammad J.

Author_Institution

Dept. of Electr. Eng., Arkansas Univ., Fayetteville, AR, USA

Volume

5

fYear

1997

fDate

10-12 Dec 1997

Firstpage

5030

Abstract

The design of recursive state estimators is considered for a general class of nonlinear stochastic discrete-time models. By deriving equations for a bound on the estimation error covariance based on a fixed-structure estimator, “bound-optimal” finite time minimum variance estimator parameters are found. The existence conditions for the statistical steady state are derived and applied to the design of constant gain estimators. A simulation result illustrates the use of such estimators

Keywords

discrete time systems; matrix algebra; nonlinear systems; optimisation; recursive estimation; state estimation; state-space methods; stochastic systems; constant gain estimators; discrete-time models; estimation error covariance; fixed-structure estimator; optimisation; recursive state estimation; state space model; stochastic nonlinear models; Estimation error; Noise measurement; Nonlinear equations; Recursive estimation; Signal design; State estimation; Steady-state; Stochastic processes; Symmetric matrices; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.649858

Filename

649858