Robust Extended Kalman Filtering for Nonlinear Systems With Stochastic Uncertainties

Author

Kai, Xiong ; Wei, Chunling ; Liu, Liangdong

Author_Institution

Nat. Lab. of Space Intell. Control, Beijing Inst. of Control Eng., Beijing, China

Volume

40

Issue

2

fYear

2010

fDate

3/1/2010 12:00:00 AM

Firstpage

399

Lastpage

405

Abstract

In this correspondence paper, a novel robust extended Kalman filter (REKF) for discrete-time nonlinear systems with stochastic uncertainties is proposed. The filter is derived to guarantee an optimized upper bound on the state estimation error covariance despite the model uncertainties as well as the linearization errors. Further analysis shows that the proposed filter has robustness against process noises, measurement noises, and model uncertainties. In addition, the new method is applied in an X-ray pulsar positioning system. It is illustrated through numerical simulations that the REKF is more effective than the standard extended Kalman filter and the extended robust H_?? filter.

Keywords

Kalman filters; discrete time systems; linearisation techniques; nonlinear systems; state estimation; stochastic processes; uncertainty handling; X-ray pulsar positioning system; discrete-time nonlinear systems; error covariance; linearization errors; measurement noises; model uncertainties; process noises; robust H_?? filter; robust extended Kalman filtering; state estimation; stochastic uncertainties; Nonlinear estimation; nonlinear uncertain system; pulsar positioning system; robust extended Kalman filter (REKF);

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on

Publisher

ieee

ISSN

1083-4427

Type

jour

DOI

10.1109/TSMCA.2009.2034836

Filename

5332373