Indoor Positioning Using Federated Kalman Filter
| dc.authorscopusid | 57202982817 | |
| dc.authorscopusid | 6603417688 | |
| dc.contributor.author | Ayabakan, T. | |
| dc.contributor.author | Kerestecioğlu, Feza | |
| dc.contributor.author | Kerestecioglu, F. | |
| dc.contributor.other | Computer Engineering | |
| dc.date.accessioned | 2023-10-19T15:05:34Z | |
| dc.date.available | 2023-10-19T15:05:34Z | |
| dc.date.issued | 2018 | |
| dc.department-temp | Ayabakan, T., Savaş Sistemleri Başmuhendisli?i, Istanbul Tersanesi Komutanli?i, Istanbul, Turkey; Kerestecioglu, F., Bilgisayar Muhendisli?i Bolumu, Kadir Has Universitesi, Istanbul, Turkey | en_US |
| dc.description | Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas | en_US |
| dc.description | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 --2 May 2018 through 5 May 2018 -- --137780 | en_US |
| dc.description.abstract | In this paper, one of the multi sensor fusion techniques, namely Federated Kalman Filter (FKF) performance is studied for Indoor Positioning problem. Kalman Filters having centralized and decentralized structures are widely used for outdoor positioning and navigation applications. Besides them, indoor positioning received large interest in recent years. In this study, decentralized FKF is applied in indoor positioning problem by taking its outdoor navigation performance into consideration. Simulations are done using distance measurements assumed to be priorly calculated. Results gathered via simulations are evaluated to be promising for future studies. © 2018 IEEE. | en_US |
| dc.identifier.citationcount | 5 | |
| dc.identifier.doi | 10.1109/SIU.2018.8404427 | en_US |
| dc.identifier.endpage | 4 | en_US |
| dc.identifier.isbn | 9781538615010 | |
| dc.identifier.scopus | 2-s2.0-85050829397 | en_US |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/SIU.2018.8404427 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/4950 | |
| dc.khas | 20231019-Scopus | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.scopus.citedbyCount | 5 | |
| dc.subject | Data fusion | en_US |
| dc.subject | Federated kalman filter | en_US |
| dc.subject | Indoor positioning | en_US |
| dc.subject | Data fusion | en_US |
| dc.subject | Indoor positioning systems | en_US |
| dc.subject | Decentralized structures | en_US |
| dc.subject | Federated Kalman filters | en_US |
| dc.subject | Indoor positioning | en_US |
| dc.subject | Multi-sensor fusion techniques | en_US |
| dc.subject | Outdoor navigation | en_US |
| dc.subject | Outdoor positioning | en_US |
| dc.subject | Kalman filters | en_US |
| dc.title | Indoor Positioning Using Federated Kalman Filter | en_US |
| dc.title.alternative | Federe Kalman Süzgeci ile Iç Mekanda Konum Belirleme | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 3b717ed5-ce95-4f19-b9d0-f544789c28da | |
| relation.isAuthorOfPublication.latestForDiscovery | 3b717ed5-ce95-4f19-b9d0-f544789c28da | |
| relation.isOrgUnitOfPublication | fd8e65fe-c3b3-4435-9682-6cccb638779c | |
| relation.isOrgUnitOfPublication.latestForDiscovery | fd8e65fe-c3b3-4435-9682-6cccb638779c |
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