基于随机可达性的PMU网络化GPS欺骗抑制与定时风险分析
GPS Spoofing Mitigation and Timing Risk Analysis in Networked PMUs via Stochastic Reachability
摘要：
为了解决PMU对欺骗的脆弱性，我们提出了一个集值状态估计技术称为随机可达性为基础的分布式卡尔曼滤波器（SR-DKF），计算安全的GPS定时在网络的接收机。利用随机可达性，我们不仅估计GPS时间，而且其随机可达集，这是通过概率zonotope（p-Zonotope）参数化。虽然仅在非欺骗条件下需要已知的测量误差界限，但我们设计了一种两层方法：我们首先通过测量创新与其预期的p-Zonotope的偏差来执行测量级欺骗缓解，其次通过估计的pZonotope与违反IEEE C37.118.1a-2014标准的不安全集合的相交概率来执行状态级时序风险分析。我们验证建议的SR-DKF进行模拟接收机网络协调信号级欺骗。我们证明了提高GPS定时精度和成功的欺骗缓解通过我们的SR-DKF。我们验证的鲁棒性估计的定时风险的接收器的数量是不同的。
英文摘要：
To address PMU vulnerability against spoofing, we propose a set-valued state estimation technique known as Stochastic Reachability-based Distributed Kalman Filter (SR-DKF) that computes secure GPS timing across a network of receivers. Utilizing stochastic reachability, we estimate not only GPS time but also its stochastic reachable set, which is parameterized via probabilistic zonotope (p-Zonotope). While requiring known measurement error bounds in only non-spoofed conditions, we design a two-tier approach: We first perform measurement-level spoofing mitigation via deviation of measurement innovation from its expected p-Zonotope and second perform state-level timing risk analysis via intersection probability of estimated pZonotope with an unsafe set that violates IEEE C37.118.1a-2014 standards. We validate the proposed SR-DKF by subjecting a simulated receiver network to coordinated signal-level spoofing. We demonstrate improved GPS timing accuracy and successful spoofing mitigation via our SR-DKF. We validate the robustness of the estimated timing risk as the number of receivers is varied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
