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dc.contributor.authorÖǧrenci, Arif Selçuk
dc.contributor.authorArsan, Taner
dc.date.accessioned2019-06-27T08:06:44Z
dc.date.available2019-06-27T08:06:44Z
dc.date.issued2018
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.urihttps://hdl.handle.net/20.500.12469/1225
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2017.09.026
dc.description.abstractThe 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.
dc.language.isoEnglish
dc.publisherPergamon-Elsevier Science Ltd
dc.subjectSource localization
dc.subjectNeural networks
dc.subjectLearning
dc.subjectReceived signal strength
dc.subjectNonlinear regression
dc.titleTransmitter source location estimation using crowd data
dc.typeArticle
dc.identifier.startpage127
dc.identifier.endpage138
dc.relation.journalComputers & Electrical Engineering
dc.identifier.volume66
dc.identifier.wosWOS:000429760300011
dc.identifier.doi10.1016/j.compeleceng.2017.09.026
dc.contributor.khasauthorÖǧrenci, Arif Selçuk
dc.contributor.khasauthorArsan, Taner


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