Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T07:32:38.307Z Has data issue: false hasContentIssue false
  • English
  • Français

SVM-based Models for Mobile Users' Initial Position Determination

Published online by Cambridge University Press:  17 June 2014

Majda Petric ,
Aleksandar Neskovic ,
Natasa Neskovic  and
Milos Borenovic
Majda Petric*
Affiliation:
(Department for Telecommunications, School of Electrical Engineering, University of Belgrade, Serbia)
Aleksandar Neskovic
Affiliation:
(Department for Telecommunications, School of Electrical Engineering, University of Belgrade, Serbia)
Natasa Neskovic
Affiliation:
(Department for Telecommunications, School of Electrical Engineering, University of Belgrade, Serbia)
Milos Borenovic
Affiliation:
(Vlatacom Research and Development Centre, Belgrade, Serbia)
*
(E-mail: majdap@etf.rs)
Get access Rights & Permissions [Opens in a new window]

Abstract

A large interest in developing commercial Location-Based Services (LBS) and the necessity of implementing emergency call services, have led to the intensive development of techniques for mobile users' localisation. In this paper, a Public Land Mobile Networks (PLMN) -based technique for initial position determination is proposed as an alternative to satellite-based methods in environments with obstructed satellite signals. Two positioning models, based on handset available Received Signal Strength (RSS) measurements from Global System for Mobile Communications (GSM) base stations and the use of Support Vector Machine (SVM) algorithms, are proposed. Performances of proposed models are verified using field measurements, collected in a suburban environment. Models are analysed in terms of positioning accuracy, complexity and latency, and compared to some other promising PLMN-based techniques. Using proposed SVM-based positioning models a median error of 4·3 m–6·2 m and latency of less than a second can be achieved.

Keywords

Initial position determinationSVMRSSField test
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 6 , November 2014 , pp. 950 - 966
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anne, K.R., Kyamakya, K., Erbas, F., Takenga, C. and Chedjou, J.C. (2004). GSM RSSI-based positioning using extended Kalman filter for training artificial neural networks. Proceedings of the 60th IEEE Vehicular Technology Conference, Los Angeles, USA.Google Scholar
Bhatia, N. and Vandana, . (2010). Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security, 8, 302–305.Google Scholar
Bishop, C.M. (2006). Pattern recognition and machine learning. Springer Science + Business Media.Google Scholar
Borkowski, J. and Lempiäinen, J. (2006). Practical network-based techniques for mobile positioning in UMTS. EURASIP Journal on Applied Signal Processing, 2006: 012930.CrossRefGoogle Scholar
Bottou, L., Chapelle, O., DeCoste, D. and Weston, J. (2007). Support Vector Machine Solvers. In: Large-Scale Kernel Machines, MIT Press.CrossRefGoogle Scholar
Burges, C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121–167.CrossRefGoogle Scholar
Dong, J., Krzyzak, A. and Suen, C.Y. (2005). Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Transactions on Pattern Analysis and Machine Learning, 27, 603–618.CrossRefGoogle Scholar
CGALIES (European Commission). (2002). Coordination Group on Access to Location Information for Emergency Services (CGALIES). Report on implementation issues related to access to location information by emergency services (E112) in the European Union. http://ec.europa.eu/echo/civil_protection/civil/pdfdocs/cgaliesfinalreportv1_0.pdf Accessed 25 March 2014.Google Scholar
FCC. (Federal Communication Commission). (2001). FCC Wireless 911 Requirements. http://transition.fcc.gov/pshs/services/911-services/enhanced911/archives/factsheet_requirements_012001.pdf Accessed 25 March 2014.Google Scholar
Filjar, R., Jezic, G. and Matijasevic, M. (2008). Location-Based Services: A Road Towards Situation Awareness. The Journal of Navigation, 61, 573–589.CrossRefGoogle Scholar
Fung, S.H., Lu, B.C. and Hsu, Y.T. (2012). Learning location from sequential signal strength based on GSM experimental data. IEEE Transactions on Vehicular Technology, 61, 726–736.CrossRefGoogle Scholar
Gun, S.R. (1998). MATLAB Support Vector Machine Toolbox. http://www.isis.ecs.soton.ac.uk/resources/svminfo/. Accessed 25 March 2014.Google Scholar
Hassoun, M.H. (1995). Fundamentals of artificial neural networks. MIT press.Google Scholar
Hsu, C.W. and Lin, C.J. (2002). A comparison of methods for multiclass Support Vector Machines. IEEE Transactions on Neural Networks, 13, 415–425.Google ScholarPubMed
Jiyan, H., Guan, G. and Qun, W. (2011). Robust location algorithm based on weighted least-squares Support Vector Machine (WLS-SVM) for non-line-of-sight environments. International Journal of the Physical Sciences, 6, 5897–5905.Google Scholar
Laitinen, H., Lahteenmaki, J. and Nordstorm, T. (2001). Database correlation method for GSM location. Proceedings of the 53rd IEEE Vehicular Technology Conference, Rhodes, Greece.CrossRefGoogle Scholar
Lakmali, B.D.S., Wijesinghe, W.H.M.P., De Silva, K.U.M., Liyanagama, K.G. and Dias, S.A.D. (2007). Design, implementation & testing of positioning techniques in mobile networks. Proceedings of the 3rd International Conference on Information and Automation for Sustainability, Melbourne, Australia.Google Scholar
Platt, J.C., (1999). Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Advances in Kernel Methods: Support Vector Machines, MIT Press.Google Scholar
Rifkin, R. and Klautau, A. (2004). In defence of one-vs.-all classification. Journal of Machine Learning Research, 5, 101–141.Google Scholar
Roos, T., Myllymäki, P. and Tirri, H. (2002). A statistical modelling approach to location estimation. IEEE Transactions on Mobile Computing, 1, 59–69.CrossRefGoogle Scholar
Shawe-Taylor, J. and Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge University Press.CrossRefGoogle Scholar
Shi, L. and Wigren, T. (2009). AECID Fingerprinting Positioning Performance. Proceedings of the IEEE GLOBECOM 2009, Honolulu, Hawaii, USA.CrossRefGoogle Scholar
Spirito, M.A. and Mattioli, A.G. (1999). Preliminary experimental results of a GSM mobile phones positioning system based on timing advance. Proceedings of the 50th IEEE Vehicular Technology Conference, Amsterdam, Netherlands.CrossRefGoogle Scholar
Sun, G. and Guo, W. (2005). Robust mobile geo-location algorithm based on LS-SVM. IEEE Transactions on Vehicular Technology, 54, 1037–1041.CrossRefGoogle Scholar
Sun, G., Chen, J., Guo, W. and Liu, K.J.R. (2005). Signal processing techniques in network-aided positioning. IEEE Signal Processing Magazine, 22, 12–23.Google Scholar
Takenga, C.M., Wen, Q. and Kyamakya, K. (2006). On the accuracy improvement issues in GSM location fingerprinting. Proceedings of the 64th IEEE Vehicular Technology Conference, Montreal, Canada.CrossRefGoogle Scholar
Wang, L., Groves, P.D. and Ziebart, M.K. (2012). Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models. The Journal of Navigation, 65, 459–476.CrossRefGoogle Scholar
Wigren, T. (2007). Adaptive Enhanced Cell-ID Fingerprinting Localization by Clustering of Precise Position Measurements. IEEE Transactions on Vehicular Technology, 56, 3199–3209.CrossRefGoogle Scholar
Wigren, T. (2012). Fingerprinting localisation using round trip time and timing advance. IET Communications, 6, 419–427.CrossRefGoogle Scholar
Wu, Z., Li, C., Ng, J.K. and Leung, K.R.P.H. (2007). Location estimation via Support Vector Regression. IEEE Transactions on Mobile Computing, 6, 311–321.CrossRefGoogle Scholar
Xuereb, D. and Debono, C.J. (2010). Mobile terminal location estimation using Support Vector Machines. Proceedings of the 4th International Symposium on Communications, Control and Signal Processing, Limassol, Cyprus.CrossRefGoogle Scholar
Yamamoto, R., Matsutani, H., Matsuki, H., Oono, T. and Ohtsuka, H. (2001). Position location technologies using signal strength in cellular systems. Proceedings of the 53rd IEEE Vehicular Technology Conference, Rhodes, Greece.CrossRefGoogle Scholar
Zaidi, R.Z. and Mark, L.B. (2005). Real-time mobility tracking algorithms for cellular networks based on Kalman filtering. IEEE Transactions on Mobile Computing, 4, 195–208.CrossRefGoogle Scholar
Zeytinci, M.B., Sari, V., Harmanci, F.K., Anarim, E. and Akar, M. (2013). Location estimation using RSS measurements with unknown path loss exponents. EURASIP Journal on Wireless Communications and Networking, 2013:178CrossRefGoogle Scholar
3GPP TS 43.059 v8.1.0. (2008). Functional stage 2 description of Location Services (LCS) in GERAN. http://www.etsi.org/deliver/etsi_ts/143000_143099/143059/08.01.00_60/ts_143059v080100p.pdf Accessed 25 March 2014.Google Scholar
3GPP TS 25.305 v8.1.0. (2008). User Equipment (UE) positioning in Universal Terrestrial Radio Access Network (UTRAN); Stage 2. http://www.etsi.org/deliver/etsi_ts/125300_125399/125305/08.01.00_60/ts_125305v080100p.pdf Accessed 25 March 2014.Google Scholar