针对多无人机系统协同导航信息精细化融合处理问题，提出了一种基于复杂网络模型的信息表示和融合推理新方法。该方法首先利用网络模型来描述无人机各个状态之间的耦合约束关系，其中网络节点表示无人机的状态，边或超边表示网络信息链，即指系统的先验信息、测量信息和动力学约束等，从而将传统状态空间导航模型抽象为网络导航模型。然后，将系统非结构化导航信息转化为结构化的费歇耳信息矩阵和信息向量，基于克拉美-罗下界不等式推导集中式和分散式最优状态估计算法，获得网络节点状态的最小方差无偏估计。通过构建100架无人机在未知环境下的自主协同导航定位场景以及数值仿真分析，验证了本文所提模型和算法的可行性。

This paper introduces a novel network-based framework for representing and fusing information for cooperative navigation of multiple Unmanned Aerial Vehicle (UAV) systems. By leveraging graph theory, the conventional state-space navigation model is abstracted into a networked navigation model, where a (hyper)network captures the coupling constraints among UAV states. In this representation, nodes correspond to the states of UAVs, while edges or hyperedges embody information links, including prior knowledge, measurement data, and system dy-namics. The unstructured navigation information is systematically transformed into a structured form via the Fisher information matrix and information vector. Based on the Cramér-Rao lower bound inequality, both cen-tralized and decentralized optimal state estimation algorithms are derived to achieve minimum variance unbiased estimation for network nodes. The feasibility and effectiveness of the proposed model and algorithms are vali-dated through numerical simulations in an autonomous cooperative navigation and positioning scenario involv-ing 100 UAVs operating in an unknown environment.