Online Monitoring and Risk Assessment of Non-Cooperative UAVs via STL-Aware Adaptive Fusion Kalman Filtering

Abstract

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.