A Hybrid Neural Network-Data Base Correlation Positioning in GSM Network

Abstract

Mobile terminal (MT) localization in a GSM environment has been of big interest in the recent years. This work exploits the advantage of position estimations from different sources in a robust fusion algorithm to reduce the positioning error. A hybrid neural network (NN)-data base correlation method (DC) is discussed. Before the fusion process, the DC position estimates are post-processed using an extra NN in order to reduce its error. Function approximation and classification properties of the NN will be investigated and the best NN architecture will be applied in the positioning algorithm. Results show that, the post processing of the DC results has a big impact on the positioning accuracy and the fusion process gets the MT estimate within a better accuracy