用图柯尔莫戈洛夫自动编码器预测无人机位置，降低通信被发现概率。
Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder
- 融合图神经网络与柯尔莫戈洛夫理论，线性化多无人机非线性运动
- 在有限历史数据下实现长期轨迹预测，检测率降低63%-75%
- 适用于需要低延迟隐蔽通信的地面自组网场景
低探测概率（LPD）通信旨在隐藏射频信号以规避监控。在利用无人飞行器（UAV）进行移动监控的场景中，由于无人机快速连续运动且动力学未知，实现LPD通信面临重大挑战。因此，准确预测无人机未来位置对实现实时LPD通信至关重要。本文提出一种新型预测隐蔽通信框架，旨在在多无人机监控下最小化地基自组网的可检测性。所提数据驱动方法将图神经网络（GNN）与柯尔莫戈洛夫理论结合，建模多无人机网络中的复杂交互，通过线性化动态过程实现长期预测，即使历史数据有限亦可。大量仿真结果表明，使用该方法预测的轨迹相比现有先进基线方法，探测概率降低至少63%至75%，展现出在实际场景中支持低延迟隐蔽操作的巨大潜力。
原文摘要 · Abstract (English)
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.
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