DocumentCode
1982635
Title
Kalman filters comparison for vehicle localization data alignment
Author
Mourllion, Benjamin ; Gruyer, Dominique ; Lambert, Alain ; Glaser, Sébastien
Author_Institution
LIVIC, INRETS/LCPC
fYear
2005
fDate
18-20 July 2005
Firstpage
178
Lastpage
185
Abstract
The aim of this paper is to carry out a comparison between several algorithms of the Kalman filters family for nonlinear systems. Alter having presented the most popular of them and showed its limitations, we introduce some new Kalman filters and compare them for the vehicle localization problem. This comparison is based on the predictive step what corresponds to the worst case that it can occur in vehicle localization. Typically, when we achieve a vehicle tracking, if the tracked vehicle is hidden, corrective data are unavailable and therefore the corrective step is disable (time data alignment)
Keywords
Kalman filters; nonlinear systems; tracking; vehicles; Kalman filters; nonlinear systems; time data alignment; vehicle localization data alignment; vehicle tracking; Filters; Gaussian noise; Jacobian matrices; Linear systems; Mobile robots; Noise measurement; Nonlinear systems; State estimation; Time measurement; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Robotics, 2005. ICAR '05. Proceedings., 12th International Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-9178-0
Type
conf
DOI
10.1109/ICAR.2005.1507410
Filename
1507410
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