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.

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.

To address the dynamic target allocation and resource scheduling problems of multiple High-Energy Laser Systems (HELSs) in defending critical infrastructure against large-scale heterogeneous UAV swarm penetration, this paper proposes a soft–hard damage cooperative task allocation and optimization method, termed Attention-based Centralized Training and Decentralized Execution proximal policy optimization (Attn-CTDE-PPO), which integrates an attention mechanism with multi-agent reinforcement learning. First, a cooperative multi-agent sequential decision-making model for multi-HELS engagement against UAV swarms is constructed by considering continuous laser irradiation, system energy consumption, thermal accumulation limits, and soft–hard damage mechanisms. Second, a multi-head attention set encoder based on a Transformer is introduced to extract global situational features at the state level. This design enables the policy network to handle a time-varying number of targets and mitigates the explosion of hybrid action spaces. At the action level, a discrete–continuous dual-head network with a masking mechanism is proposed to achieve target allocation and laser-parameter scheduling. Furthermore, a multi-dimensional threat assessment module is developed, which can prune infeasible actions in the action space via physical rule-based masking, thereby accelerating the decision-making speed of the method. Monte Carlo simulation experiments verified the defense reliability, energy management efficiency and real-time responsiveness of the proposed method. The experimental results demonstrate that, in heterogeneous swarm scenarios with different numbers of UAVs, the proposed method effectively suppresses UAV penetration, improves the defense success rate, and reduces total system energy consumption compared with baseline algorithms.

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.