Low Probability of Detection (LPD) communication aims to obscure the very
presence of radio frequency (RF) signals, going beyond just hiding the content
of the communication. However, the use of Unmanned Aerial Vehicles (UAVs)
introduces a challenge, as UAVs can detect RF signals from the ground by
hovering over specific areas of interest. With the growing utilization of UAVs
in modern surveillance, there is a crucial need for a thorough understanding of
their unknown nonlinear dynamic trajectories to effectively implement LPD
communication. Unfortunately, this critical information is often not readily
available, posing a significant hurdle in LPD communication. To address this
issue, we consider a case-study for enabling terrestrial LPD communication in
the presence of multiple UAVs that are engaged in surveillance. We introduce a
novel framework that combines graph neural networks (GNN) with Koopman theory
to predict the trajectories of multiple fixed-wing UAVs over an extended
prediction horizon. Using the predicted UAV locations, we enable LPD
communication in a terrestrial ad-hoc network by controlling nodes' transmit
powers to keep the received power at UAVs' predicted locations minimized. Our
extensive simulations validate the efficacy of the proposed framework in
accurately predicting the trajectories of multiple UAVs, thereby effectively
establishing LPD communication.

通过将图神经网络与库普曼理论相结合，我们引入了一个新的框架，用于预测多个固定翼无人机在一个延伸预测时间内的轨迹，从而实现地面 LPD 通信。通过控制节点的发送功率，使接收功率在无人机预测位置保持最小化，我们能够在地面自组网中实现 LPD 通信。通过广泛的模拟，验证了该提议框架在准确预测多个无人机轨迹和有效建立 LPD 通信方面的有效性。