Abstract
The Global Positioning System (GPS) aided low cost Dead Reckoning (DR) system can provide without interruption the vehicle position for efficient fleet management solutions in smart cities. The Extended Kalman Filter (EKF) is generally applied for data fusion using the sensor’s measures and the GPS position as a helper.
However, the EKF depends on the vehicle dynamic variations and may quickly diverge during periods of GPS signal loss.
In this paper, we present a robust low cost approach using EKF and neural networks (NN) with Particle Swarm Optimization (PSO) to reliably estimate the real time vehicle position. While GPS signals are available, we train the NN with PSO on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 94% over the simple EKF predictions in case of GPS failures.
This work was financially supported by the National Center of Scientific and Technical Research (CNRST), Morocco.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in Europe. J. Urban Technol. 18, 65–82 (2011). Routledge, England
Noureldin, A., Karamat, T.B., Eberts, M.D., El-Shafie, A.: Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Trans. Veh. Technol. 58, 1077–1096 (2009). IEEE
Grewal, M.S.: Kalman filtering. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science, pp. 705–708. Springer, Heidelberg (2011)
Domenico, D.D., Fiengo, G., Glielmo, L.: On-board prototype of a vehicle localization system. In: 2007 IEEE International Conference on Control Applications, pp. 438–443. IEEE, USA (2007)
Georgy, J., Iqbal, U., Noureldin, A.: Quantitative comparison between Kalman filter and Particle filter for low cost INS/GPS integration. In: Proceedings of the 6th International Symposium on Mechatronics and Its Applications (ISMA 2009), pp. 1–7. IEEE, USA (2009)
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)
Ismaeel, S.A., Hasan, A.M.: GPS/INS system integratin based on neuro-wavelet techniques. In: Proceedings of the 2006 International Conference on Artificial Intelligence (ICAI 2006), vol. 2, pp. 446–452. CSREA Press, Las Vegas (2006)
Hasan, A.M., Samsudin, K., Ramli, A.R.: Optimizing of ANFIS for estimating INS error during GPS outages. J. Chin. Inst. Eng. 34, 967–982 (2011). Taylor & Francis, London
Malleswaran, M., Vaidehi, V., Sivasankari, N.: A novel approach to the integration of GPS and INS using recurrent neural networks with evolutionary optimization techniques. In: Aerospace Science and Technology, vol. 32, pp. 169–179. Elsevier B.V., Netherlands (2014)
Belhajem, I., Ben Maissa, Y., Tamtaoui, A.: A hybrid low cost approach using Extended Kalman filter and neural networks for real time positioning. In: 2016 International Conference on Information Technology for Organizations Development (IT4OD), pp. 1–5. IEEE (2016)
Belhajem, I., Ben Maissa, Y., Tamtaoui, A.: A hybrid machine learning based low cost approach for real time vehicle position estimation in a smart city. In: El-Azouzi, R., Menasché, D.S., Sabir, E., Pellegrini, F.D., Benjillali, M. (eds.) Advances in Ubiquitous Networking 2. LNEE, vol. 397, pp. 559–572. Springer, Heidelberg (2017). doi:10.1007/978-981-10-1627-1_44
Goodall, C., Syed, Z., El-Sheimy, N.: Improving INS/GPS navigation accuracy through compensation of Kalman filter errors. In: Proceedings of the 64th IEEE Vehicular Technology Conference, VTC Fall 2006, pp. 1–5. IEEE (2006)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, USA (1994)
Yegnanarayana, B.: Artificial Neural Networks. Prentice-Hall of India Pvt. Ltd., New Delhi (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE, USA (1995)
Kulkarni, R.V., Venayagamoorthy, G.K.: Particle swarm optimization in wireless-sensor networks a brief survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41, 262–267 (2011). IEEE Press, Piscataway, NJ, USA
Korrapati, H., Courbon, J., Alizon, S., Marmoiton, F.: The Institut Pascal data sets: un jeu de données en extérieur, multicapteurs et datées avec réalité terrain, données d’étalonnage et outils logiciels. In: ORASIS 2013, Congrès des jeunes chercheurs en vision par ordinateur. France (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Belhajem, I., Ben Maissa, Y., Tamtaoui, A. (2017). An Improved Robust Low Cost Approach for Real Time Vehicle Positioning in a Smart City. In: Maglaras, L., Janicke, H., Jones, K. (eds) Industrial Networks and Intelligent Systems. INISCOM 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-319-52569-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-52569-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52568-6
Online ISBN: 978-3-319-52569-3
eBook Packages: Computer ScienceComputer Science (R0)