Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3899
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKaraman, Serife Senem-
dc.contributor.authorAkarsu, Alper-
dc.contributor.authorGirici, Tolga-
dc.date.accessioned2020-10-22T16:46:31Z-
dc.date.available2020-10-22T16:46:31Z-
dc.date.issued2019-06
dc.identifier.citationKaraman, S. S., Akarsu, A. and Girici, T. (2019, June). Use of Particle Filtering in RSSI-Based Localization by Drone Base Stations. In 2019 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-5). IEEE.en_US
dc.identifier.isbn978-172811243-5
dc.identifier.issn2472-4386
dc.identifier.urihttps://hdl.handle.net/20.500.11851/3899-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8909133-
dc.description.abstractDrone Base Stations (DBSs) provide flexible deployment and line-of-sight coverage opportunities, which led to many use cases, such as broadband Internet, military, surveillance, agriculture etc. DBSs can optimize and adapt their positions based on user location information. Especially in GPS-denied tactical scenarios ground user location estimation is an important problem. In this work we investigate particle filter as a method of user position estimation. We utilize the recently proposed air-to-ground pathloss model for RSSI-based location estimation. We investigate different DBS trajectories and various resampling methods. Finally, we show by simulations that particle filtering performs comparably to maximum likelihood estimation, which makes it a suitable alternative for localization and tracking.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2019 International Symposium on Networks, Computers and Communications, ISNCC 2019en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLocalizationen_US
dc.subjectdrone base stationsen_US
dc.subjectUAV estimationen_US
dc.subjectparticle filteren_US
dc.subjecttrackingen_US
dc.titleUse of Particle Filtering in RSSI-Based Localization by Drone Base Stationsen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.authorid0000-0002-5705-4806-
dc.identifier.wosWOS:000520478600047en_US
dc.identifier.scopus2-s2.0-85075938089en_US
dc.institutionauthorGirici, Tolga-
dc.identifier.doi10.1109/ISNCC.2019.8909133-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

1
checked on Mar 23, 2024

WEB OF SCIENCETM
Citations

4
checked on Mar 23, 2024

Page view(s)

66
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.