EKF-based state estimation for train localization

Damien Veillard 1 Frédérick Mailly 2 Philippe Fraisse 3
2 SysMIC - Conception et Test de Systèmes MICroélectroniques
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Determination of longitudinal acceleration of a land-vehicle regardless its inclination is a common problem for systems of localization. This paper addresses a solution for railway applications by combining a low-cost MEMS IMU (Inertial Measurement Unit) equipped with a 3-axis accelerometer and a 3-axis gyrometer and an algorithm for data fusion. In particular, the impact of adding attitude and velocity observations into a Kalman filter is studied. Compared to conventional methods that use regular Kalman filter with external aiding sensors such as GPS or tachometers, the proposed approach uses an Extended Kalman Filter which exploits an augmented state vector. A velocity estimation obtained by a method observing the spectral analysis of the vertical accelerometer and the attitude estimation obtained by a complementary filter compose the observation vector with the accelerometer and the gyrometer data. At last, experimental results performed on an urban train are presented.
Type de document :
Communication dans un congrès
IEEE Sensors 2016 , Oct 2016, Orlando, United States. 2016, Proceedings of IEEE Sensors 2016 Conference. 〈http://ieee-sensors2016.org〉. 〈10.1109/ICSENS.2016.7808726〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01445352
Contributeur : Frédérick Mailly <>
Soumis le : mardi 24 janvier 2017 - 17:47:17
Dernière modification le : jeudi 28 juin 2018 - 17:53:17

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Damien Veillard, Frédérick Mailly, Philippe Fraisse. EKF-based state estimation for train localization. IEEE Sensors 2016 , Oct 2016, Orlando, United States. 2016, Proceedings of IEEE Sensors 2016 Conference. 〈http://ieee-sensors2016.org〉. 〈10.1109/ICSENS.2016.7808726〉. 〈lirmm-01445352〉

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