DocumentCode
461483
Title
An Adaptive Iterated Kalman Filter
Author
Yong-An Zhang ; Di Zhou ; Guang-Ren Duan
Author_Institution
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China. zhangyongan@hit.edu.cn
fYear
2006
fDate
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
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, China
Print_ISBN
7-302-13922-9
Electronic_ISBN
7-900718-14-1
Type
conf
DOI
10.1109/CESA.2006.313591
Filename
4105657
Link To Document