Automatic Positioning of Mobile Users via GSM Signal Measurements

Today the need for mobile communication systems and the high increase in the number of users have also made the development of new generation mobile applications indispensable. Obtaining location information has been one of the most interesting and significant areas of improvement. The purpose of the services used to determine the location is generally to obtain the information of the users such as approximate location, speed, and time. The GPS is the most preferred and globally accurate positioning system among global positioning systems. However, in addition high installation cost of the system; galactic and meteorological factors, high buildings, other physical obstacles, and especially indoor areas are some of the main constraints that can lead to serious signal degradation and losses which may cause the system to be out of service. In this context, there is an urgent need for positioning systems that will be alternative and complementary to global positioning systems. The cellular network is widely used by almost everyone and its coverage area is increasing day by day. The network has been trained and tested in the simulation environment using machine learning algorithms, namely, extreme learning machine (ELM), generalized regression neural network (GRNN), and ? nearest neighborhood (?NN). When compared to other cellular localization methods in the literature, the proposed system performs positioning with much higher accuracies with distance error rates below a meter (m) at minimum, and between 76-216 m on average. The test results show that it can successfully localize the mobile users with a significant accuracy for indoor, where GPS signals are very weak or cannot be received at all; and it can also stand in the breach for outdoor, where GPS may be disabled for different reasons.

PDF

___

[1] Sevindi, C. (2005). (Global Positioning System (GPS) and Its Usage in Geographical Researches. Turkish Journal Geographical Sciences, 3(1), 101-112.
[2] Teunissen, P., & Montenbruck, O. (Eds.). (2017). Springer handbook of global navigation satellite systems. Springer.
[3] Magro, M. J., & Debono, C. J. (2007, September). A genetic algorithm approach to user location estimation in umts networks. In EUROCON 2007-The International Conference on" Computer as a Tool" (pp. 1136-1139). IEEE.
[4] Türkyılmaz, O. (2007). Environment aware location estimation in cellular networks. Master Thesis. Boğaziçi University, İstanbul.
[5] Kurt, Ö. F. (2009) Location estimation by fingerprinting in cellular networks. Master Thesis. Boğaziçi University, İstanbul.
[6] Fritsche, C., Klein, A., & Wurtz, D. (2009, March). Hybrid GPS/GSM localization of mobile terminals using the extended Kalman filter. In 2009 6th Workshop on Positioning, Navigation and Communication (pp. 189-194). IEEE.
[7] Xuereb, D., & Debono, C. J. (2010, March). Mobile terminal location estimation using Support Vector Machines. In 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP) (pp. 1-4). IEEE.
[8] Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
[9] Ertuğrul, Ö. F., & Kaya, Y. (2014). A detailed analysis on extreme learning machine and novel approaches based on ELM. American Journal of computer science and engineering, 1(5), 43-50.
[10] Öztekin, A., & Erçelebi, E. (2019). An efficient soft demapper for APSK signals using extreme learning machine. Neural Computing and Applications, 31(10), 5715-5727.
[11] Huang, G. B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: a survey. International journal of machine learning and cybernetics, 2(2), 107-122.
[12] Celikoglu, H. B., & Cigizoglu, H. K. (2007). Public transportation trip flow modeling with generalized regression neural networks. Advances in Engineering Software, 38(2), 71-79.
[13] Cigizoglu, H. K., & Alp, M. (2006). Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software, 37(2), 63-68.
[14] Kim, B., Lee, D. W., Park, K. Y., Choi, S. R., & Choi, S. (2004). Prediction of plasma etching using a randomized generalized regression neural network. Vacuum, 76(1), 37-43.
[15] Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10), 1482-1484.
[16] Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
[17] Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
[18] Coomans, D., & Massart, D. L. (1982). Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-Nearest neighbour classification by using alternative voting rules. Analytica Chimica Acta, 136, 15-27.
[19] Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine learning, 6(1), 37-66.
[20] Shmueli, G., Patel, N. R., & Bruce, P. C. (2011). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. John Wiley and Sons.
[21] Duda, R. O., & Hart, P. E. (2006). Pattern classification. John Wiley & Sons.
[22] Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Miscellaneous clustering methods. Cluster analysis, 215-255.
[23] Hall, P., Park, B. U., & Samworth, R. J. (2008). Choice of neighbor order in nearest- neighbor classification. the Annals of Statistics, 36(5), 2135-2152.