Automatic Positioning of Mobile Users via GSM Signal Measurements

Year 2021, , 152 - 160, 30.04.2021

Ercan Demir Abdulkerim Öztekin

https://doi.org/10.17694/bajece.852963

Cited By: 1

Abstract

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 system is the most preferred and globally accurate positioning system among global positioning systems. However, in addition to requiring a high installation cost of this system, it is one of the biggest constraints that galactic and meteorological factors, high buildings and other physical obstacles, and especially closed areas can lead to serious signal weaknesses and losses which may cause the system to be out of service. Considering these issues, it is seen that 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 employed data sets were created by recording the received signal strength (RSS), location information of the GSM base station and the user measured in indoor and outdoor areas through a mobile application we have developed in the Android Studio environment for mobile phones. The network has been trained by machine learning algorithms; extreme learning machine (ELM), generalized regression neural network (GRNN) and k nearest neighborhood (kNN). In the tests conducted with indoor, outdoor and combined data sets, it has been observed that the proposed positioning system works well with distance error rates below a meter (m) at the minimum, and between 76-216 m on average.

Keywords

GSM positioning, GPS, localization, machine learning

References

• [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.