Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
528269 | Information Fusion | 2013 | 10 Pages |
Abstract
In vehicle positioning applications, the confidence level in the position and velocity estimates can be even more significant than accuracy. In this study, a probabilistic interval method is proposed, which combines, through union and intersection operations, the information from a possibly uncertain predictor (the vehicle model) and measurement sensors. The proposed method is compared to Kalman filtering and to guaranteed interval estimation in the context of railway vehicles where security is the key objective.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
G. Goffaux, M. Remy, A. Vande Wouwer,