论文标题
飞行传感器数据和基于波束形成的集成无人机跟踪，并使用高斯流程回归进行通道估计
Flight Sensor Data and Beamforming based Integrated UAV Tracking with Channel Estimation using Gaussian Process Regression
论文作者
论文摘要
随着对无人机（UAV）应用的爆炸性增加，需要可靠的链接获取用于服务无人机。考虑到无人机的动态特征，坚持一个可靠的链接而没有光束未对准是非常具有挑战性的。在本文中，我们提出了一个基于基于集成的无人机跟踪方案来解决此问题的飞行传感器数据和光束成型信号。考虑到飞行传感器数据的实际规范和波束成形的飞行员信号，该方案提供了兼容的集成系统。 UAV位置跟踪由两个步骤组成：1）使用高斯过程回归（GPR）方法，通过飞行传感器数据数据和2）位置更新，这是一种非参数机器学习。飞行传感器数据可以协助地面站（GS）或无人机节点设计预编码和接收光束矩阵，并大幅减少开销。波束形成信号即使没有飞行传感器数据，也可以实现远光增益。因此，提出的方案可以通过利用这两个信号连续支持移动目标。提供了仿真结果，以确认所提出的方案的表现优于其他常规光束跟踪方案。我们还从角值估计的平均绝对误差（MAE）中得出了3维（3D）波束形成增益和光谱效率（SE），可以用作数据传输链路的波束成式性能指标。
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.
