Low Probability of Detection (LPD) communication aims to obscure the presence of radio frequency (RF) signals to evade surveillance. In the context of mobile surveillance utilizing unmanned aerial vehicles (UAVs), achieving LPD communication presents significant challenges due to the UAVs' rapid and continuous movements, which are characterized by unknown nonlinear dynamics. Therefore, accurately predicting future locations of UAVs is essential for enabling real-time LPD communication. In this paper, we introduce a novel framework termed predictive covert communication, aimed at minimizing detectability in terrestrial ad-hoc networks under multi-UAV surveillance. Our data-driven method synergistically integrates graph neural networks (GNN) with Koopman theory to model the complex interactions within a multi-UAV network and facilitating long-term predictions by linearizing the dynamics, even with limited historical data. Extensive simulation results substantiate that the predicted trajectories using our method result in at least 63%-75% lower probability of detection when compared to well-known state-of-the-art baseline approaches, showing promise in enabling low-latency covert operations in practical scenarios.

翻译：低检测概率（LPD）通信旨在隐藏射频（RF）信号的存在以规避监视。在利用无人飞行器（UAV）进行移动监视的背景下，由于UAV快速且持续的运动（其特征为未知的非线性动力学），实现LPD通信面临重大挑战。因此，准确预测UAV的未来位置对于实现实时LPD通信至关重要。本文提出了一种称为预测性隐蔽通信的新框架，旨在最小化多UAV监视下地面自组织网络的可检测性。我们的数据驱动方法协同整合了图神经网络（GNN）与Koopman理论，以建模多UAV网络内的复杂交互，并通过线性化动力学（即使在历史数据有限的情况下）促进长期预测。大量仿真结果证实，与知名的先进基线方法相比，使用我们方法预测的轨迹可实现至少低63%-75%的检测概率，展现了在实际场景中实现低延迟隐蔽操作的前景。