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As the growth of communication and satellite industry, the demand of satellite antenna evaluation is increasing. Particularly Communication On The Move (COTM) terminal antenna, including electronically steerable antennas (ESA) and for the communication between new constellations on LEO and MEO, requires tracking accuracy test for the communication on moving vehicles. The measurement capability of conventional methodologies have been limited due to their location fixed facilities and non-adjustable sensor's positions during the measurement. To overcome this drawbacks, we will present how multi-agent system of UAVs could be utilized for COTM tracking accuracy evaluation. This measurement needs instant actions for UAVs to keep them navigating in order to achieve accurate and stable measurement. Reinforcement learning (RL) techniques are investigated for this purpose in this paper. The performance improvement is demonstrated with the system using RL technique to adjust UAVs with sensors during the measurement.

Along 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.

Along with the growth of communication and satellite industry, the importance of satellite antenna evaluation is increasing. Particularly Communication On The Move (COTM) terminal antenna, including the communication between new types of constellations on LEO and MEO, requires tracking accuracy test for the communication on moving vehicles. The conventional test facilities are locally fixed and lack flexibility. To make the antenna measurement more accessible, we are developing a methodology for in-situ measurement by introducing multiple Unmanned-Aerial-Vehicles (UAVs) system with RF payload. Thanks to the dynamic flexibility of UAVs, this system can flexibly change the test configuration on site and make new test scenarios available, such as emulating the orbit of non-GEO satellites during the measurement. However, one of the challenges of the proposed system is the additional uncertainties during the measurement due to the mobility of UAVs. To overcome this challenge, we design recursive stochastic filtering and fusion approaches, and evaluate their estimation performance via numerical simulations. By introducing stochastic filter and fusion algorithms, the effect of error is mitigated, and better accuracy can be achieved compared to an existing method. This project is performed in collaboration with Cranfield University in the UK and QuadSAT in Denmark.

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