Development and design of applications for UAV-based satellite communication terminal antenna evaluation using deep-reinforcement learning

dc.contributor.advisorShin, Hyo-Sang
dc.contributor.advisorTsourdos, Antonios
dc.contributor.authorOmi, Sake
dc.date.accessioned2025-06-11T13:38:45Z
dc.date.available2025-06-11T13:38:45Z
dc.date.freetoread2025-06-11
dc.date.issued2023-08
dc.descriptionTsourdos, Antonios - Associate Supervisor
dc.description.abstractIn recent years, satellites are launched almost on a daily basis and most of them are to be operated in Non-Geostationary Orbit (NGSO). The number of user terminals communicating with satellites is rapidly increasing. The interference has become a serious issue due to the crowded communication environment and the increased popularity of NGSO. Utilization of NGSO adds more complexity for operating user terminals since it requires tracking the satellite which is not static from the terminals’ points of view. Also, the risk of interference has escalated due to the greater demand for Satellite-communication-On-The-Move (SOTM), which involves the need to keep terminals constantly pointing toward the target satellite while they are installed on a moving object. To ensure a safe communication environment, the terminal antenna must be verified based on set requirements. However, the test process at conventional test facilities is inefficient and does not have a solution to test antennas in the new communication scenarios. Therefore, this thesis aims to develop in-situ Unmanned Aerial Vehicle (UAV) -based measurement applications that are autonomously guided to enhance the efficiency of the measurement and to propose novel measurement methods to verify the antennas operated in new environments. Utilizing UAVs and performing measurements onsite is challenging due to the additional error sources and uncertainties in measurements and sensor positioning. In this work, a new deep-reinforcement learning algorithm is developed which can adapt to the dynamic environment under the presence of disturbances. Using this algorithm, the applications to verify the boresight angle offset of terminal antennas and to evaluate SOTM terminal antennas are proposed. The proposed applications are tested based on the numerical simulations and the results showed that the developed applications improved the efficiency of measurements and satisfied the required measurement accuracy. The thesis investigates novel measurement approaches for a new generation of satellite communication aiming to respond to the measurement demands that currently have no solution.
dc.description.coursenamePhD in Aerospace
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24027
dc.language.isoen
dc.publisherCranfield University
dc.publisher.departmentSATM
dc.rights© Cranfield University, 2023. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.
dc.subjectUAV-based Antenna measurement
dc.subjectNon-geostationary orbit
dc.subjectboresigt angle localization
dc.subjectSatellite communication
dc.subjectVSAT
dc.subjectSatellite-Communication-On-The-Move
dc.subjectdepointing
dc.subjectphased array antenna
dc.subjectreinforcement learning
dc.subjectTD3
dc.subjectrecurrent neural network
dc.subjectPOMDP
dc.subjectrobustness
dc.subjectcausality
dc.titleDevelopment and design of applications for UAV-based satellite communication terminal antenna evaluation using deep-reinforcement learning
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhD

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