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-asset UAV safety monitoring scenarios, dynamic threat assessment is challenged by delayed recognition of protected-asset shifts and unstable threat rankings under noisy observations. To address these issues, this paper proposes an approach-directionality-based dynamic UAV threat assessment method that integrates historical trend correction and adaptive exponential moving average (EMA) smoothing. The proposed method takes each UAV–protected asset pair as the basic assessment unit and constructs a threat matrix for multiple UAVs and multiple protected assets. Based on fundamental threat factors, including UAV type, closing speed, altitude, and distance, an approach directionality factor is defined using the geometric relationship between the UAV velocity direction and the bearing direction of each protected asset. To capture shifts in the potentially affected protected asset, the historical directionality sequence is linearly fitted, and the instantaneous directionality is dynamically enhanced or suppressed according to the estimated trend. An adaptive EMA mechanism is then introduced to improve temporal stability under noisy conditions. Simulation results from turning-approach scenarios, Monte Carlo experiments, and noise robustness tests show that the proposed method identifies protected-asset shifts earlier while maintaining high protected-asset identification accuracy and ranking stability. The framework provides interpretable support for threat ranking, situational awareness, and safety-oriented decision-making in non-offensive UAV safety monitoring applications.