Automatic Positioning of Mobile Users via GSM Signal Measurements

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

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