Multi-Sensor Indoor Positioning

gdc.relation.journal UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering en_US
dc.contributor.author Ayabakan, Tarık
dc.contributor.author Kerestecioğlu, Feza
dc.contributor.other Computer Engineering
dc.contributor.other 05. Faculty of Engineering and Natural Sciences
dc.contributor.other 01. Kadir Has University
dc.date.accessioned 2020-12-24T13:29:06Z
dc.date.available 2020-12-24T13:29:06Z
dc.date.issued 2019
dc.description.abstract In this paper, the performance of three different kinds of Kalman Filter (KF) structure: Single Kalman Filter (SKF)(which filters data of a single sensor), Centralized Kalman Filter (CKF) and Federated Kalman Filter (FKF) are studied considering the indoor positioning problem. Kalman filters are widely used in outdoor positioning and navigation applications providing good results. For multi-sensor applications, KF has centralized and decentralized structures. In this study, multisensor and single-sensor dedicated Kalman filter structures FKF, CKF and SKF are applied in indoor positioning problem by taking its outdoor navigation performance into consideration. Received Signal Strength (RSS) data are generated, from which distance information, a vital part of the simulations, are obtained. Three different noise levels are used to assess performance of filters. Results gathered via different simulations showed that multi-sensor structures provide a better solution than single sensor structures. en_US
dc.identifier.citationcount 3
dc.identifier.doi 10.1109/UBMK.2019.8907082 en_US
dc.identifier.isbn 978-172813964-7
dc.identifier.scopus 2-s2.0-85076203301 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/3656
dc.identifier.uri https://doi.org/10.1109/UBMK.2019.8907082
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 2019 4th International Conference on Computer Science and Engineering (UBMK)
dc.rights info:eu-repo/semantics/embargoedAccess en_US
dc.subject data fusion en_US
dc.subject indoor positioning en_US
dc.subject Kalman filter en_US
dc.subject multi-sensor systems en_US
dc.title Multi-Sensor Indoor Positioning en_US
dc.type Conference Object en_US
dspace.entity.type Publication
gdc.author.institutional Kerestecioğlu, Feza
gdc.bip.impulseclass C5
gdc.bip.influenceclass C5
gdc.bip.popularityclass C4
gdc.coar.access embargoed access
gdc.coar.type text::conference output
gdc.description.endpage 335 en_US
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 330 en_US
gdc.description.volume 09/01/19 en_US
gdc.description.wosquality N/A
gdc.identifier.openalex W2989597334
gdc.identifier.wos WOS:000609879900062 en_US
gdc.oaire.diamondjournal false
gdc.oaire.impulse 3.0
gdc.oaire.influence 3.1645286E-9
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gdc.oaire.keywords data fusion
gdc.oaire.keywords multi-sensor systems
gdc.oaire.keywords indoor positioning
gdc.oaire.keywords Kalman filter
gdc.oaire.popularity 7.347639E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 01 natural sciences
gdc.oaire.sciencefields 0104 chemical sciences
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gdc.opencitations.count 6
gdc.plumx.crossrefcites 3
gdc.plumx.mendeley 8
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gdc.scopus.citedcount 6
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