Omi, SakiShin, HyosangTsourdos, AntoniosEspeland, JoakimBuchi, Andrian2022-03-142022-03-142022-02-11Omi S, Hyo-Sang S, Tsourdos A, et al., (2022) Reinforcement learning system of UAV for antenna beam localization. In: 2021 IEEE Conference on Antenna Measurements & Applications (CAMA), 15-17 November 2021, Antibes Juan-les-Pins, France, pp. 61-65978-1-7281-9698-52643-6795https://doi.org/10.1109/CAMA49227.2021.9703640https://dspace.lib.cranfield.ac.uk/handle/1826/17655Along with the growth of satellite communication industry, the demands and benefits to perform satellite terminal antenna evaluation are increasing. UAV based in-situ measurement can increase the efficiency of the measurement procedure. Main beam localization is a necessary procedure to execute the antenna evaluation test. To accelerate the process of finding the antenna beam centre, this paper develop a meta-reinforcement learning based algorithm. The developed algorithm is compared with other methods and it showed the best performance in terms of accuracy, robustness and travelling efficiency not only in the simulated radiation pattern environment but also in the empirically obtained radiation pattern.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/antenna measurementUAV measurementbeam localizationmeta-reinforcement learningConference paper978-1-7281-9697-82474-1760