Unmanned aerial vehicle positioning using 5G new radio technology in urban environment

dc.contributor.authorMousa, Morad
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2024-01-03T16:18:52Z
dc.date.available2024-01-03T16:18:52Z
dc.date.issued2023-11-10
dc.description.abstractUnmanned aerial vehicles (UAVs) are becoming increasingly popular for various applications, including surveillance, monitoring, mapping, delivery, and inspection. However, their positioning capabilities in urban environments can be limited due to challenges such as Non-Line-of-Sight (NLOS) propagation, multi-path interference, and signal blockage caused by tall buildings, trees, and other obstacles, which can affect their positioning capabilities. The purpose of this paper is to provide a novel approach for UAV’s positioning based on Observed Time Difference of Arrival (OTDOA), combining 5G (NR) technology and an inertial measurement unit (IMU) to improve UAV positioning in urban environments. Integrating these technologies can improve UAV positioning and control systems by offering rapid, low-latency communication, a thorough and precise comprehension of the UAV’s surroundings and its own condition, and more accurate assessments of the UAV’s location, speed, and orientation. Simulation model shows the data from these sensors is then fused using an Extended Kalman Filter (EKF) to estimate the UAV’s position and orientation. The study shows that the proposed system delivers accurate and reliable UAV positioning in these environments, outperforming traditional methods.en_UK
dc.identifier.citationMousa M, Al-Rubaye S, Inalhan G. (2023) Unmanned aerial vehicle positioning using 5G new radio technology in urban environment. In: 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), 1-5 October 2023, Barcelona, Spainen_UK
dc.identifier.eisbn979-8-3503-3357-2
dc.identifier.eissn2155-7209
dc.identifier.isbn979-8-3503-3358-9
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC58513.2023.10311106
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20596
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subject5G networksen_UK
dc.subjectpositioningen_UK
dc.subjectUAVen_UK
dc.subjectIMUen_UK
dc.subjectbarometric pressure sensorsen_UK
dc.subjectExtended Kalman Filter (EKF)en_UK
dc.titleUnmanned aerial vehicle positioning using 5G new radio technology in urban environmenten_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2023-04-22

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