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With explosively increasing demands for unmanned aerial vehicle (UAV)applications, reliable link acquisition for serving UAVs is required.Considering the dynamic characteristics of UAV, it is hugely challenging topersist a reliable link without beam misalignment. In this paper, we propose aflight sensor data and beamforming signal based integrated UAV tracking schemeto deal with this problem. The proposed scheme provides a compatible integratedsystem considering the practical specification of the flight sensor data andthe beamforming pilot signal. The UAV position tracking is comprised of twosteps: 1) UAV position prediction by the flight sensor data and 2) positionupdate with the beamforming signal using Gaussian process regression (GPR)method, which is a nonparametric machine learning. The flight sensor data canassist ground station (GS) or UAV nodes in designing the precoding and thereceive beamforming matrix with drastically reduced overheads. The beamformingsignal can accomplish high beamforming gain to be maintained even when theflight sensor data is absent. Therefore, the proposed scheme can support themoving target continuously by utilizing these two signals. The simulationresults are provided to confirm that the proposed scheme outperforms otherconventional beam tracking schemes. We also derive 3-dimensional (3D)beamforming gain and spectral efficiency (SE) from the mean absolute error(MAE) of the angular value estimation, which can be used as beamformingperformance metrics of the data transmission link in advance.

Flight Sensor Data and Beamforming based Integrated UAV Tracking with Channel Estimation using Gaussian Process Regression