Reliable real-time 3D localization is essential for multi-UAV navigation, collision avoidance, and coordinated flight, yet onboard estimates can degrade under GNSS multipath, non-line-of-sight reception, vertical drift, and intentional interference. This paper presents a decentralized, lightweight 3D position-refinement layer that improves robustness by fusing each Unmanned Aerial Vehicle (UAV)'s local estimate with neighbor-shared state summaries and inter-UAV range or proximity constraints. The method performs uncertainty-aware neighborhood fusion by weighting each UAV's prior according to its reported covariance and weighting neighbor constraints according to link quality, ranging uncertainty, and a learned trust score. To support practical deployment, the framework explicitly handles cold start and temporary localization loss by inflating or substituting weak priors, allowing trusted neighborhood constraints to bootstrap and stabilize estimates until absolute sensing recovers. To mitigate the impact of faulty or malicious participants, each UAV applies a local range-consistency check, smoothed over time, to down-weight or exclude neighbors whose reported positions are incompatible with observed inter-UAV distances. Simulation experiments with 10 UAVs in a 3D volume show that the proposed refinement substantially reduces mean localization error during cold start, remains competitive after local estimators stabilize, and maintains lower error as the fraction of malicious nodes increases compared with fusion without trust. These results suggest that the approach can serve as a practical resilience layer for swarm operation in challenging environments.

翻译：可靠的三维实时定位对于多无人机编队导航、碰撞规避及协同飞行至关重要，然而在GNSS多路径、非视距传播、垂直漂移及人为干扰等条件下，机载估计精度会显著下降。本文提出一种轻量级去中心化三维位置精化层，通过融合各无人机的局部估计值、邻机共享状态摘要及无人机间距离/邻近约束信息来提升定位鲁棒性。该方法实施不确定性感知的邻域融合策略：根据各无人机上报的协方差矩阵对其先验信息加权，并依据链路质量、测距不确定性及学习信任度对邻机约束进行权重分配。为支持实际部署，框架通过膨胀或替代弱先验信息显式处理冷启动与临时定位丢失问题，允许可信邻域约束引导并稳定估计直至绝对感知恢复。针对故障或恶意节点影响，每架无人机执行基于时序平滑的局部测距一致性校验，对位置信息与观测距离不兼容的邻节点降低权重或予以排除。在三维空间内10架无人机的仿真实验表明：所提精化方法在冷启动阶段显著降低平均定位误差，在局部估计器稳定后保持竞争性精度，且随着恶意节点比例增加，相较于未引入信任机制的融合方法仍维持更低误差。这些结果表明该方法可作为群体无人机在挑战环境下运行的实用弹性增强层。