Transmitter source location estimation using crowd data. (February 2018)

• Record Type: Journal Article
• Authors: Ogrenci, Arif Selcuk; Arsan, Taner
• Is Part Of: Computers & electrical engineering. Volume 66(2018)
• Page Start: 127
• Page End: 138
• Publication Date: 2018-02
• Subjects: Source localization -- Neural networks -- Learning -- Received signal strength -- Nonlinear regression; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.09.026
• Languages: English
• ISSNs: 0045-7906

Abstract

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.