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A pairwise dynamic threat assessment method that combines three components that provides an interpretable basis for asset-level threat prioritization and the assessment of temporally concentrated UAV approaches is proposed.
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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.
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@article{Wu2026Pairwise,
title = {Pairwise Dynamic Threat Assessment for Multi-UAV and Multi-Asset Scenarios Considering Heading Directionality and Cooperative Pressure},
author = {Min Wu and Lin Zhang and Bo Zhang and 王文峰 and 屈辉 and Mingang Zhang},
journal = {Drones},
year = {2026},
doi = {10.3390/drones10110800},
url = {https://doi.org/10.3390/drones10110800}
}
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