Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder

TL;DR

This paper presents a novel graph Koopman autoencoder framework that predicts UAV trajectories to enable low-detectability covert communication in surveillance scenarios, significantly reducing detection probability.

Contribution

It introduces a data-driven method combining GNN and Koopman theory for accurate long-term UAV trajectory prediction with limited data, enhancing covert communication capabilities.

Findings

Achieves 63%-75% lower detection probability compared to baseline methods.

Effectively models complex UAV interactions with limited historical data.

Enables real-time, low-latency covert operations in multi-UAV environments.

Abstract

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.