Indoor location estimation by using maximum likelihood estimation based algorithm on small cell networks

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dc.contributor.advisorBayat, Oğuz
dc.contributor.authorİlyas, Muhammad
dc.date.accessioned2021-05-15T16:31:35Z
dc.date.available2021-05-15T16:31:35Z
dc.date.issued2014
dc.departmentAltınbaş Üniversitesi, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.descriptionYüksek Lisans
dc.description.abstractÖzet için ingilizce bölüme başvurunuz. Çevisi daha sonra eklenecektir.en_US
dc.description.abstractThis thesis presents a new framework for indoor localization using Third Generation(3G) Universal Mobile Telephone System (UMTS) Femtocell. The fingerprinting technique is applied to collect the Received Signal Strength Indication(RSSI) values through an Android User Equipment(UE) and data is processed in real time using MQTT server. To achieve better Radio Frequency (RF) planning and optimization for the placement of Femto Access Point(FAP), statistical analysis is performed by normalizing and calculating the Mean Squared Error(MSE) of the acquired data. To maximize the success rate in finding the location of the person, Maximum likelihood Estimation(MLE) based algorithm is used for tracking. Simulation was carried out both for 1 million samples and real life test using 100 samples. To make it more accurate and efficient, MLE based algorithm is developed and simulated in MATLAB. Both simulation approaches resulted in good success rates. A case study is also part of this thesis, case study discuss about the real life project and deployment setup for indoor positioning estimation and localization techniques using multiple FAP's.en_US
dc.identifier.citationIlyas, Muhammad. (2014). Indoor location estimation by using maximum likelihood estimation based algorithm on small cell networks. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.en_US
dc.identifier.endpage1en_US
dc.identifier.startpage77en_US
dc.identifier.urihttps://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=WY5CM7tPNE2z_YM6pBu0twFdt94eGH4b7k_mj9UHFS7mLgtOgCtg6SakdG0QhH9T
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1648
dc.identifier.yoktezid416219
dc.institutionauthorİlyas, Muhammad
dc.language.isoen
dc.publisherAltınbaş Üniversitesien_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIndoor Localizationen_US
dc.subjectIndoor Pozitioningen_US
dc.subjectLocation Estimationen_US
dc.subjectFemtocellsen_US
dc.subjectMaximum Likelihood Estimationen_US
dc.subjectMQTTen_US
dc.subjectNormalizationen_US
dc.subjectMean Squared Erroren_US
dc.subjectFingerPrintingen_US
dc.subjectSmall Cellsen_US
dc.titleIndoor location estimation by using maximum likelihood estimation based algorithm on small cell networks
dc.title.alternativeKüçük hücre ağlarında en yüksek olabilirlik kestirimi tabanlı bina içi yer kestirimi algoritması
dc.typeMaster Thesis

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