Three-Dimensional Dynamic UAV Threat Assessment Using Approach Directionality and Historical-Trend Correction for Multi-Asset Protection

In multi-asset UAV safety monitoring, dynamic threat assessment is challenged by delayed recognition of changes in the potentially threatened asset and unstable threat rankings under noisy observations. This paper proposes an interpretable three-dimensional dynamic threat assessment method integrating approach directionality, historical trend correction, and adaptive exponential moving average smoothing. Each UAV-protected asset pair is treated as an assessment unit to construct a many-to-many threat matrix. A basic threat score is first derived from UAV type, three-dimensional closing velocity, distance, altitude difference, and vertical approach motion. Approach directionality is then characterized using the geometric alignment between the UAV velocity and the bearing to each protected asset, with optional attitude and reachability information. The temporal trend of directionality is estimated from historical observations to capture persistent changes in approach tendency, while adaptive smoothing balances responsiveness to genuine threat transitions against suppression of noise-induced fluctuations. Comprehensive simulation experiments, including three-dimensional discrimination, Monte Carlo evaluation, observation noise, latency, missed detections, clutter, difficult motion conditions, and ablation studies, demonstrate that the proposed method provides accurate and timely threat identification while substantially improving score and ranking stability. The resulting framework offers an interpretable and computationally efficient basis for multi-UAV threat prioritization and safety-oriented situational awareness.