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.

In multi-UAV and multi-asset scenarios, dynamic threat assessment needs to distinguish how each UAV moves relative to different assets while also accounting for the temporal concentration of multiple approaches. To address this, this study proposes a pairwise dynamic threat assessment method that combines three components. First, distance decay, heading directionality, and radial closing velocity are integrated to characterize the kinematic threat of each UAV–asset pair. Second, a group-level pressure term based on the temporal consistency of estimated protection zone entry times is introduced to represent temporally concentrated multi-UAV approaches. Third, the pairwise and group-level components are fused through a bounded risk mapping and updated using innovation-adaptive exponential smoothing, which balances responsiveness to abrupt changes against score stability. Monte Carlo simulations with synthetic two-dimensional trajectories were conducted to evaluate ranking performance, temporal response, noise robustness, parameter sensitivity, cooperative pressure behavior, and computational cost. The results show that the proposed method outperforms the distance-only baseline in most trajectory classes, that the contribution of the cooperative pressure term depends on the evaluation reference, that adaptive smoothing responds faster to abrupt transitions than Fixed EMA, and that the computational cost is acceptable under the tested conditions. Overall, the proposed framework provides an interpretable basis for asset-level threat prioritization and the assessment of temporally concentrated UAV approaches.