关键词: 多无人机系统, 协同导航, 超网络模型, 费歇耳信息, 信息融合

Abstract: 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.

Key words: multi-UAV system, cooperative navigation, hypernetwork model, Fisher information, information fusion