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Title: Use of Particle Filtering in RSSI-Based Localization by Drone Base Stations
Authors: Karaman, Serife Senem
Akarsu, Alper
Girici, Tolga
Keywords: Localization
drone base stations
UAV estimation
particle filter
Issue Date: Jun-2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Karaman, 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.
Abstract: Drone 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.
ISBN: 978-172811243-5
ISSN: 2472-4386
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

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