NDLI: A theoretical analysis of the Extended Kalman Filter for data fusion in vehicular positioning

Content Provider	IEEE Xplore Digital Library
Author	Toledo-Moreo, R. Gruyer, D. Lambert, A.
Copyright Year	2011
Description	Author affiliation: IFSTTAR (formerly LIVIC), France (Gruyer, D.) \|\| Department of Electronics and Computer Technology at the Technical University of Cartagena, Spain. 30202 (Toledo-Moreo, R.) \|\| IEF, Univ. Paris Sud-11, CNRS, F-91405 (Lambert, A.)
Abstract	Global Navigation Satellite Systems (GNSS) offer a great value for many location-based services and applications. However, due to their limitations in terms of coverage, continuity, accuracy and integrity, GNSS are often fused with some extra aiding sensors. To perform the data fusion of multiple sensors it is possible to find in the literature of the field a large number of approaches that claim better accuracy, efficiency in computational terms or robustness than a reference one that is given for comparison. Normally, this reference is the Extended Kalman Filter (EKF), the most common version of the Kalman Filter for non-linear systems. However, because sensors, tests, filter tunings, etc. vary largely from one publication to another, it is not possible in many occasions to have a clear idea of the real benefits of the different methods in fair terms. This paper presents a theoretical analysis of the goodness of the EKF in loosely coupled data fusion architectures. The methodology presented can be applied to understand the limitations of different approaches for fusing multiple sensors in non-linear systems. Illustrations depict a real case with a sensor-set consisting of a GNSS, a gyro and the odometry of a road vehicle.
Starting Page	305
Ending Page	310
File Size	288185
Page Count	6
File Format	PDF
ISBN	9781612846682
e-ISBN	9781612846712
DOI	10.1109/ITST.2011.6060063
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-23
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data Fusion Extended Kalman Filter Sensors Kalman filters Mathematical model Approximation methods Equations Vehicle Positioning Vehicles Global Positioning System
Content Type	Text
Resource Type	Article

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