  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 to
persist a reliable link without beam misalignment. In this paper, we propose a
flight sensor data and beamforming signal based integrated UAV tracking scheme
to deal with this problem. The proposed scheme provides a compatible integrated
system considering the practical specification of the flight sensor data and
the beamforming pilot signal. The UAV position tracking is comprised of two
steps: 1) UAV position prediction by the flight sensor data and 2) position
update with the beamforming signal using Gaussian process regression (GPR)
method, which is a nonparametric machine learning. The flight sensor data can
assist ground station (GS) or UAV nodes in designing the precoding and the
receive beamforming matrix with drastically reduced overheads. The beamforming
signal can accomplish high beamforming gain to be maintained even when the
flight sensor data is absent. Therefore, the proposed scheme can support the
moving target continuously by utilizing these two signals. The simulation
results are provided to confirm that the proposed scheme outperforms other
conventional 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 beamforming
performance metrics of the data transmission link in advance.
