WSN Aided Indoor Localization for Unmanned Vehicles

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dc.authoridALTUN, Yusuf/0000-0002-2099-0959
dc.authoridTuna, Gurkan/0000-0002-6466-4696
dc.authoridMumcu, Tarık Veli/0000-0002-8995-9300
dc.authorwosidALTUN, Yusuf/AAA-9929-2020
dc.authorwosidTuna, Gurkan/AAG-4412-2019
dc.authorwosidMumcu, Tarık Veli/D-5045-2019
dc.contributor.authorTuna, Gurkan
dc.contributor.authorAltun, Yusuf
dc.contributor.authorMumcu, Tarik Veli
dc.contributor.authorGulez, Kayhan
dc.date.accessioned2024-06-12T11:16:57Z
dc.date.available2024-06-12T11:16:57Z
dc.date.issued2012
dc.departmentTrakya Üniversitesien_US
dc.description8th International Conference on Intelligent Computing (ICIC) -- JUL 25-29, 2012 -- Huangshan, PEOPLES R CHINAen_US
dc.description.abstractThis paper presents design considerations of an Extended Kalman Filter (EKF) based Wireless Sensor Network (WSN) aided indoor localization for unmanned vehicles (UV). In this approach, we integrate Received Signal Strength Indicator (RSSI) measurements into an EKF based localization system. The localization system primarily uses measurements from a Laser Range Finder (LRF) and keeps track of the current position of the UV using an EKF-based algorithm. The integration of RSSI measurements at predetermined intervals improves the accuracy of the localization system. It may also prevent large drifts from the ground truth, kidnapping, and loop closure errors. Player/Stage based simulation studies were conducted to prove the effectiveness of the proposed system. The results of the comparative simulations show that integrating RSSI measurements into the localization system improves the system's accuracy.en_US
dc.description.sponsorshipIEEE Computat Intelligence Soc,Int Neural Network Soc,Natl Sci Fdn China,Tongji Univen_US
dc.description.sponsorshipYildiz Technical University Scientific Research Projects Coordination Department [2010- 04- 02ODAP01, 2010- 04- 02- KAP05]en_US
dc.description.sponsorshipThis research has been supported by Yildiz Technical University Scientific Research Projects Coordination Department. Project Number: 2010- 04- 02ODAP01 and Project Number: 2010- 04- 02- KAP05.en_US
dc.identifier.endpage533en_US
dc.identifier.isbn978-3-642-31575-6
dc.identifier.isbn978-3-642-31576-3
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.startpage526en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14551/24503
dc.identifier.volume7390en_US
dc.identifier.wosWOS:000314766200067en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringer-Verlag Berlinen_US
dc.relation.ispartofIntelligent Computing Theories And Applications, Icic 2012en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUnmanned Vehicleen_US
dc.subjectLocalizationen_US
dc.subjectWsnsen_US
dc.subjectEKFen_US
dc.subjectPlayer/Stageen_US
dc.titleWSN Aided Indoor Localization for Unmanned Vehiclesen_US
dc.typeConference Objecten_US

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