New results on stochastic stability of discrete-time Unscented Kalman Filter

Author

Dymirkovsky, Gyorgyi

Author_Institution

Syst. Eng. Dept., SS Cyril & Methodius Univ., Skopje, Macedonia

fYear

2012

fDate

18-20 July 2012

Firstpage

1543

Lastpage

1548

Abstract

Performance of the Unscented Kalman Filter, UKF, for nonlinear stochastic discrete-time systems is investigated. It is proved that under certain conditions, the estimation error of the UKF remains bounded. Furthermore, it is shown that the design of noise covariance matrix plays an important role in improving the stability of the UKF algorithm. It is further shown the estimation error remains bounded the nonlinear observability rank condition is satisfied. These results are verified by numerical simulations for a relevant illustrative example.

Keywords

Kalman filters; covariance matrices; discrete time systems; nonlinear control systems; observability; stability; stochastic systems; discrete-time unscented Kalman filter; estimation error; noise covariance matrix; nonlinear observability rank condition; nonlinear stochastic discrete-time systems; stochastic stability; Covariance matrix; Estimation error; Kalman filters; Noise; Nonlinear systems; Observability; Stability analysis; Kalman filtering; discrete-time filters; nonlinear stochstic systems; stochastic stability; unscented Kalman filters;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4577-2118-2

Type

conf

DOI

10.1109/ICIEA.2012.6360969

Filename

6360969