基于复杂网络模型的多无人机系统协同导航信息融合方法

doi:10.7527/S1000-6893.2025.32428

Abstract

Abstract:

To address the problem of refined information fusion processing in multi-UAV cooperative navigation systems， a new method for information representation and fusion reasoning based on a complex network model is proposed. 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 dynamics. 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 centralized 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 validated through numerical simulations in an autonomous cooperative navigation and positioning scenario involving 100 UAVs operating in an unknown environment.

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

CLC Number:

V279

Peng GUO, Tianlai XU, Anqi LANG, Hutao CUI, Zidi LI. A complex network-based information fusion approach for cooperative navigation of multi-UAV systems[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(5): 332428.

Figures/Tables 8

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情形	最大绝对定位误差/m	最大相对导航定位误差/m	路标点平均定位误差/m	计算用时/s
1	29.15	7.02		432
2	6.43	7.64		553
3	3.79	3.64	6.08	1 626
4	3.11	3.89	5.85	2 447

A complex network-based information fusion approach for cooperative navigation of multi-UAV systems

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