On the Accuracy Improvement Issues in GSM Location Fingerprinting

Determining the position of mobile users in GSM networks has become more and more important. Such services as emergency calls and other location dependent services have been of great importance in the last years. A key factor for the success of any localization technology is its accuracy. This work is focused on localization in a dense urban scenario or in any other area where the GPS signal is not available or its error is very big due to some obstruction of the satellites. Different methods such as those based on neural network localization, database correlation, dead reckoning and a tracking algorithm in case of user mobility have been examined in this work in order to find the optimal in terms of accuracy. The pre-processing of the received signal strengths (rss) is performed to reduce the positioning error due to the rss-stochastic behaviour. Results show that, a tracking algorithm using NN positioning results and an extended Kalman filter (EKF) supplies better results in case of mobility of the user.