RSSI-Based Indoor Positioning via Adaptive Federated Kalman Filter
| dc.contributor.author | Ayabakan, Tarik | |
| dc.contributor.author | Kerestecioglu, Feza | |
| dc.date.accessioned | 2023-10-19T15:11:54Z | |
| dc.date.available | 2023-10-19T15:11:54Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In this paper, federated Kalman filter (FKF) is applied for indoor positioning. Position information that is multi-laterated from the distance information obtained using the received signal strengths collected from several access points are processed in a FKF to estimate the position of the target. Two approaches are presented to adjust the information-sharing coefficients of FKF using online measurements. The data collected on a test bed composed of four access points are used to assess and compare the performances of the proposed algorithms. It is shown that the estimation error can be improved considerably by adjusting the information-sharing coefficients online. | en_US |
| dc.identifier.citationcount | 4 | |
| dc.identifier.doi | 10.1109/JSEN.2021.3097249 | en_US |
| dc.identifier.issn | 1530-437X | |
| dc.identifier.issn | 1558-1748 | |
| dc.identifier.issn | 2379-9153 | |
| dc.identifier.scopus | 2-s2.0-85110823145 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/JSEN.2021.3097249 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/5273 | |
| dc.khas | 20231019-WoS | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE-Inst Electrical Electronics Engineers Inc | en_US |
| dc.relation.ispartof | Ieee Sensors Journal | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Sensors | en_US |
| dc.subject | Kalman filters | en_US |
| dc.subject | Sensor fusion | en_US |
| dc.subject | Mathematical model | en_US |
| dc.subject | Sensor systems | en_US |
| dc.subject | Position measurement | en_US |
| dc.subject | Location awareness | en_US |
| dc.subject | Indoor positioning | en_US |
| dc.subject | Kalman filter | en_US |
| dc.subject | sensor fusion | en_US |
| dc.title | RSSI-Based Indoor Positioning via Adaptive Federated Kalman Filter | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.author.id | AYABAKAN, TARIK/0000-0003-0605-0378 | |
| gdc.author.id | Kerestecioglu, Feza/0000-0001-9722-9458 | |
| gdc.author.institutional | Kerestecioğlu, Feza | |
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| gdc.bip.popularityclass | C4 | |
| gdc.coar.access | metadata only access | |
| gdc.coar.type | text::journal::journal article | |
| gdc.description.departmenttemp | [Ayabakan, Tarik] Kadir Has Univ, Dept Elect Elect Engn, TR-34083 Istanbul, Turkey; [Ayabakan, Tarik] TCG Alemdar Command, TR-34083 Istanbul, Turkey; [Kerestecioglu, Feza] Kadir Has Univ, Dept Comp Engn, TR-34083 Istanbul, Turkey | en_US |
| gdc.description.endpage | 5308 | en_US |
| gdc.description.issue | 6 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q1 | |
| gdc.description.startpage | 5302 | en_US |
| gdc.description.volume | 22 | en_US |
| gdc.description.wosquality | Q2 | |
| gdc.identifier.openalex | W3184331394 | |
| gdc.identifier.wos | WOS:000770054800053 | en_US |
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| gdc.oaire.keywords | sensor fusion | |
| gdc.oaire.keywords | Sensor fusion | |
| gdc.oaire.keywords | Sensor systems | |
| gdc.oaire.keywords | Mathematical model | |
| gdc.oaire.keywords | Indoor positioning | |
| gdc.oaire.keywords | Sensors | |
| gdc.oaire.keywords | Location awareness | |
| gdc.oaire.keywords | Position measurement | |
| gdc.oaire.keywords | Kalman filter | |
| gdc.oaire.keywords | Kalman filters | |
| gdc.oaire.popularity | 7.902427E-9 | |
| gdc.oaire.publicfunded | false | |
| gdc.oaire.sciencefields | 0202 electrical engineering, electronic engineering, information engineering | |
| gdc.oaire.sciencefields | 02 engineering and technology | |
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| gdc.opencitations.count | 9 | |
| gdc.plumx.crossrefcites | 4 | |
| gdc.plumx.mendeley | 8 | |
| gdc.plumx.scopuscites | 14 | |
| gdc.scopus.citedcount | 14 | |
| gdc.wos.citedcount | 12 | |
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