Determination of Positioning Accuracies by Using Fingerprint Localisation and Artificial Neural Networks

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dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2019-09-08T12:09:31Z
dc.date.available2019-09-08T12:09:31Z
dc.date.issued2019en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractFingerprint localisation technique is an effective positioning technique to determine the object locations by using radio signal strength, values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localisation technique is deployed by using classical k-nearest neighborhood method to determine the unknown object locations. Additionally, several artificial neural networks, are employed, using fingerprint data, such as single-layer feed forward neural network multi-layer feed forward neural network, multi-layer back propagation neural network general regression neural network, and deep neural network to determine the same unknown object locations. Fingerprint database is built by received signal strength indicator measurement signatures across the grid locations. The construction and the adapted approach of different neural networks using the fingerprint data are described. The results of them are compared with the classical k-nearest neighborhood method and it was found that deep neural network was the best neural network technique providing the maximum positioning accuracies.en_US
dc.identifier.doi10.2298/TSCI180912334Ken_US
dc.identifier.endpageS111en_US
dc.identifier.issn0354-9836
dc.identifier.issn2334-7163
dc.identifier.scopus2-s2.0-85065158650en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpageS99en_US
dc.identifier.urihttps://hdl.handle.net/11363/1443
dc.identifier.urihttps://doi.org/
dc.identifier.volume23en_US
dc.identifier.wosWOS:000465190400011en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherVINCA INST NUCLEAR SCI, MIHAJLA PETROVICA-ALASA 12-14 VINCA, 11037 BELGRADE. POB 522, BELGRADE, 11001, SERBIAen_US
dc.relation.ispartofTHERMAL SCIENCEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectreceived signal strength indicatoren_US
dc.subjectk-nearest neighborhooden_US
dc.subjectartificial neural networksen_US
dc.subjectsingle-layer feed forward neural networken_US
dc.subjectmulti-layer feed forward neural networken_US
dc.subjectmulti-layer back propagation neural networken_US
dc.subjectdeep neural networken_US
dc.subjectThermodynamicsen_US
dc.titleDetermination of Positioning Accuracies by Using Fingerprint Localisation and Artificial Neural Networksen_US
dc.typeArticleen_US

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