Transmitter Source Location Estimation Using Crowd Data

Abstract

The problem of transmitter source localization in a dense urban area has been investigated where a supervised learning approach utilizing neural networks has been adopted. The cellular phone network cells and signals have been used as the test bed where data are collected by means of a smart phone. Location and signal strength data are obtained by random navigation and this information is used to develop a learning system for cells with known base station location. The model is applied to data collected in other cells to predict their base station locations. Results are consistent and indicating a potential for effective use of this methodology. The performance increases by increasing the training set size. Several shortcomings and future research topics are discussed. (C) 2017 Elsevier Ltd. All rights reserved.