标题： 基于强化学习的无人机防撞中实时贝叶斯漂移逃避GNSS欺骗检测 显示英文标题

标题： Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction

摘要： 自主无人飞行器（UAV）依赖全球导航卫星系统（GNSS）伪距测量进行精确的实时定位和导航。 然而，这种依赖性使它们面临复杂的欺骗威胁，其中对手操纵伪距以欺骗UAV接收器。 其中，逃避漂移的欺骗攻击微妙地扰动测量值，逐渐偏离UAV的轨迹而不触发传统的信号级防欺骗机制。 传统的分布偏移检测技术通常需要积累一定数量的样本，导致延迟，阻碍快速检测和及时响应。 因此，稳健的时间尺度检测方法对于识别攻击开始并利用替代传感模态进行应急规划至关重要，从而提高对隐蔽对抗操作的弹性。 本研究探讨了一种贝叶斯在线变化点检测（BOCPD）方法，该方法监测强化学习（RL）批评者网络中的价值估计的时间变化，以检测UAV导航中的细微行为偏差。 实验结果表明，这种基于时间价值的框架优于传统的GNSS欺骗检测器、时间半监督学习框架和Page-Hinkley检验，在逃避漂移的欺骗攻击中实现了更高的检测准确率和更低的误报率和漏报率。 显示英文摘要

摘要： Autonomous unmanned aerial vehicles (UAVs) rely on global navigation satellite system (GNSS) pseudorange measurements for accurate real-time localization and navigation. However, this dependence exposes them to sophisticated spoofing threats, where adversaries manipulate pseudoranges to deceive UAV receivers. Among these, drift-evasive spoofing attacks subtly perturb measurements, gradually diverting the UAVs trajectory without triggering conventional signal-level anti-spoofing mechanisms. Traditional distributional shift detection techniques often require accumulating a threshold number of samples, causing delays that impede rapid detection and timely response. Consequently, robust temporal-scale detection methods are essential to identify attack onset and enable contingency planning with alternative sensing modalities, improving resilience against stealthy adversarial manipulations. This study explores a Bayesian online change point detection (BOCPD) approach that monitors temporal shifts in value estimates from a reinforcement learning (RL) critic network to detect subtle behavioural deviations in UAV navigation. Experimental results show that this temporal value-based framework outperforms conventional GNSS spoofing detectors, temporal semi-supervised learning frameworks, and the Page-Hinkley test, achieving higher detection accuracy and lower false-positive and false-negative rates for drift-evasive spoofing attacks.