DocumentCode
2487013
Title
A non-divergent estimation algorithm in the presence of unknown correlations
Author
Julier, Simon J. ; Uhlmann, Jeffrey K.
Author_Institution
Robotics Res. Group, Oxford Univ., UK
Volume
4
fYear
1997
fDate
4-6 Jun 1997
Firstpage
2369
Abstract
This paper addresses the problem of estimation when the cross-correlation in the errors between different random variables are unknown. A new data fusion algorithm, the covariance intersection algorithm (CI), is presented. It is proved that this algorithm yields consistent estimates irrespective of the actual correlations. This property is illustrated in an application of decentralised estimation where it is impossible to consistently use a Kalman filter
Keywords
filtering theory; sensor fusion; state estimation; covariance intersection algorithm; cross-correlation; data fusion algorithm; nondivergent estimation algorithm; random variables; unknown correlations; Covariance matrix; Information filtering; Information filters; Predictive models; Random variables; Sensor fusion; Sensor systems; State estimation; Vehicles; Yield estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1997. Proceedings of the 1997
Conference_Location
Albuquerque, NM
ISSN
0743-1619
Print_ISBN
0-7803-3832-4
Type
conf
DOI
10.1109/ACC.1997.609105
Filename
609105
Link To Document