AI 中文总结
针对异步异构传感与不确定运动模式下非合作UAV的在线状态估计与风险评估问题，提出融合交互式多模型多速率卡尔曼滤波与STL的框架，提升了估计精度与安全预警的及时性，可用于实时UAV安全监测。
AI 中文摘要
本文研究存在异步异构传感与不确定运动模式时非合作无人机（UAV）的在线状态估计与预测风险评估问题。为解决该问题，开发了统一的估计与安全评估框架，将交互式多模型多速率卡尔曼滤波与信号时序逻辑（STL）相融合，该框架能在同一递归架构内同时实现低层状态跟踪与高层安全推理。其主要贡献是提出一种STL感知的时变模式转移机制，利用形式化安全规范诱导的鲁棒性指标在线更新模型概率，通过将安全语义直接嵌入模式推理与估计过程，提升了对机动变化、传感异步性及演化威胁模式的响应能力。基于估计的状态分布，该框架进一步生成多步状态预测与概率可达集，用于有限时域安全评估与风险触发式预警生成，因此该方法不仅能提供当前目标状态估计，还能在不安全行为完全可观测前发出早期预警。最后，从实时UAV监测平台获得的实验结果表明，所提方法提升了估计精度，且能生成更早、信息量更丰富的安全预警，证明了其在实时UAV监视与安全监测应用中的有效性。
英文摘要
This paper considers the problem of online state estimation and predictive risk assessment for non-cooperative unmanned aerial vehicles (UAVs) in the presence of asynchronous heterogeneous sensing and uncertain motion modes. To address this problem, a unified estimation and safety-assessment framework is developed by integrating an interacting multiple-model multi-rate Kalman filter with signal temporal logic (STL). The proposed framework enables simultaneous low-level state tracking and high-level safety reasoning within a common recursive architecture. Its main contribution is an STL-aware time-varying mode transition mechanism that updates model probabilities online using robustness measures induced by formal safety specifications. By embedding safety semantics directly into the mode inference and estimation process, the method improves responsiveness to maneuver variations, sensing asynchrony, and evolving threat patterns. Based on the estimated state distributions, the framework further generates multi-step state predictions and probabilistic reachable sets, which are used for finite-horizon safety evaluation and risk-triggered warning generation. Consequently, the proposed method provides not only estimates of the current target state, but also early indication of unsafe behaviors before they become fully observable. Finally, experimental results obtained from a real-time UAV monitoring platform show that the proposed approach improves estimation accuracy and produces earlier and more informative safety warnings, demonstrating its effectiveness for real-time UAV surveillance and safety monitoring applications.
Comments13 pages, 7 figures
