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dc.contributor.advisorKerestecioglu, Fezaen_US
dc.contributor.authorAYABAKAN, TARIK
dc.date.accessioned2023-07-25T07:28:50Z
dc.date.available2023-07-25T07:28:50Z
dc.date.issued2022-01
dc.identifier.urihttps://hdl.handle.net/20.500.12469/4360
dc.description.abstractIn this study, multi-sensor indoor positioning methods, which fuse the tri-laterated position data of the target are considered. The lateration is based on the dis tances that are obtained using the signal strengths received from different Wi-Fi access points. A new method, which is based on federated Kalman filtering (FKF) and makes use of the fingerprint data, namely, federated Kalman filter with skipped covariance updating (FKF-SCU) is proposed for indoor positioning. After that chal lenging issue of FKF, information sharing coefficient assignment is studied and two online adaptation methods based on received signal strength indication (RSSI) and distance information gathered from APs are proposed. Lastly, FKF-SCU structure is combined with adaptive FKF configuration. The data collected on two different test beds are used to compare the performance of the proposed positioning methods to those of the regular federated and centralized filters. It is shown on the test data that these algorithms improve the position accuracy and provide fault tolerance whenever signal reception is interrupted from an access point.en_US
dc.language.isoengen_US
dc.publisherKadir Has Üniversitesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIndoor Positioningen_US
dc.subjectFederated Kalman Filteren_US
dc.subjectSensor Fusionen_US
dc.subjectFault Toleranceen_US
dc.titleMulti-sensor indoor positioningen_US
dc.typedoctoralThesisen_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektronik Mühendisliği Ana Bilim Dalıen_US
dc.relation.publicationcategoryTezen_US
dc.identifier.yoktezid726354en_US


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