Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/20419
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dc.contributor.authorTrivodaliev, Kireen_US
dc.contributor.authorRisteska Stojkoska, Biljanaen_US
dc.contributor.authorPalikrushev, Jordanen_US
dc.contributor.authorKalajdziski, Slobodanen_US
dc.date.accessioned2022-07-05T07:56:41Z-
dc.date.available2022-07-05T07:56:41Z-
dc.date.issued2017-07-06-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/20419-
dc.description.abstractNowadays, finding the Unmanned Aerial Vehicle (UAV) position in the absence of GPS is attractive and challenging problem in the research community. In this paper, we present a novel algorithm for mini UAV indoor localization based on distance measurements between the UAV and the existing infrastructure consisting of WiFi Access Points. Our algorithm uses two well-known techniques from the literature: Multi-dimensional Scaling (MDS) and Weighted Centroid Localization (WCL). Through extensive simulations we have shown that our algorithm is very suitable for indoor localization of mini UAVs. For small radio-range error, our algorithm exhibits a small localization error of less than 5% of the radio range.en_US
dc.publisherIEEEen_US
dc.subjectindoor localization; positioning; unmanned aerial vehicle; multidimensional scaling; weighted centroiden_US
dc.titleIndoor localization of unmanned aerial vehicles based on RSSIen_US
dc.typeProceeding articleen_US
dc.relation.conferenceIEEE EUROCON 2017-17th International Conference on Smart Technologiesen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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