An Adaptive Iterated Kalman Filter

Author

Zhang, YongAn ; Zhou, Di ; Duan, Guang-Ren

Author_Institution

Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen

Volume

2

fYear

2006

fDate

4-6 Oct. 2006

Firstpage

1727

Lastpage

1730

Abstract

The recursive filtering of discrete-time nonlinear systems in the presence of unknown noise statistical parameters is studied. By embedding the modified Sage-Husa noise statistics estimator into the iterated Kalman filter, an adaptive iterated Kalman filter is obtained. With iterative operations as well as the online estimation of unknown covariance of virtual noise, linearized error can be reduced. As a result, the estimation performance is improved. A numerical example shows the effectiveness of the proposed filter.

Keywords

Kalman filters; adaptive control; discrete time systems; nonlinear control systems; statistical analysis; adaptive iterated Kalman filter; discrete-time nonlinear systems; modified Sage-Husa noise statistics estimator; recursive filtering; Adaptive control; Adaptive filters; Filtering; Noise measurement; Nonlinear systems; Programmable control; Recursive estimation; Statistics; Systems engineering and theory; Taylor series; Estimation; adaptive filter; extended Kalman filter; iterated Kalman filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Engineering in Systems Applications, IMACS Multiconference on

Conference_Location

Beijing

Print_ISBN

7-302-13922-9

Electronic_ISBN

7-900718-14-1

Type

conf

DOI

10.1109/CESA.2006.4281916

Filename

4281916