Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Airborne Antenna Switching Strategy Using Deep Learning on UAV Line-Of-Sight Datalink System

  • Received : 2018.10.26
  • Accepted : 2018.11.27
  • Published : 2018.12.31
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Abstract

In the Unmanned Aerial Vehicle Line-Of-Sight datalink system, there is a possibility that the communication line is disconnected because line of sight can not be secured by one antenna due to changes in position and posture of the air vehicle. In order to prevent this, both top and bottom of air vehicle are equipped with antennas. At this time, if the signal can be transmitted and received by switching to an antenna advantageous for securing the line of sight, communication disconnection can be minimized. The legacy antenna switching method has disadvantages such that diffraction, fading due to the surface or obstacles, interference and reflection of the air vehicle are not considered, or antenna switching standard is not clear. In this paper, we propose an airborne antenna switching method for improving the performance of UAV LOS datalink system. In the antenna switching method, the performance of each of the upper and lower parts of the mounted antenna according to the position and attitude of the air vehicle is predicted by using the deep learning in an UAV LOS datalink system in which only the antenna except the receiver is duplicated. Simulation using flying test dataset shows that it is possible to switch antennas considering the position and attitude of unmanned aerial vehicle in the datalink system.

Keywords

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Fig. 1. LOS Datalink System

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Fig. 2. Using airborne bottom antenna

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Fig. 3. Using airborne top antenna

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Fig. 4. Distance between GDT and virtual points

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Fig. 5. Concept for ECEF coordinate system transformation and air vehicle elevation

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Fig. 6. RPM(Rssi Prediction Model)

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Fig. 7. RPM for three input parameters

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Fig. 8. RPM for six input parameters

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Fig. 9. Procedure of antenna switching using RSSI prediction of airborne antenna

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Fig. 10. Training result of each models

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Fig. 11. Histogram for RSSI prediction model of top antenna

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Fig. 12. Histogram for RSSI prediction model of bottom antenna

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Fig. 13. Test Environment

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Fig. 14. Test Equipment

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Fig. 15. Antenna switching during simulation

Table 1. Training, Validation, Test loss of each models

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