A Hybrid Machine Learning Based Low Cost Approach for Real Time Vehicle Position Estimation in a Smart City-Reference-Cited by-同舟云学术

A Hybrid Machine Learning Based Low Cost Approach for Real Time Vehicle Position Estimation in a Smart City

Published:2016-11-04 Issue: Volume: Page:559-572
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Belhajem Ikram,Ben Maissa Yann,Tamtaoui Ahmed

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-1627-1_44

Reference14 articles.

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3. Grewal, M.S., Weill, L.R., Andrews, A.P.: Global Positioning Systems, Inertial Navigation, and Integration. Wiley-Interscience, New York (2007)

4. Zamora-Izquierdo, M.A., Betaille, D.F., Peyret, F., Joly, C.: Comparative study of Extended Kalman Filter, Linearised Kalman Filter and Particle Filter applied to low-cost GPS-based hybrid positioning system for land vehicles. In: International Journal of Intelligent Information and Database Systems, vol. 2, pp. 149–166. Inderscience Publishers, Geneva (2008)

5. Lucet, E., Betaille, D., Donnay Fleury, N., Ortiz, M., Salle, D., Canou, J.: Real-time 2D localization of a car-like mobile robot using dead reckoning and GPS, with satellite masking prediction. In: Accurate localization for land transportation Workshop, pp. 15–18. France (2009)

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