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<p>Low Earth Orbit (LEO) satellites are characterized by high received signal power and high signal propagation rate, and their pseudorange accuracy far exceeds traditional navigation constellations. However, the current LEO satellite’s beam coverage is small, resulting in a single UAV only able to receive 1–2 satellite beams, rendering positioning impossible. To address this issue, this paper proposes a cooperative positioning method for UAV clusters. This method uses the ranging information between UAV (Unmanned Aerial Vehicle) clusters for pseudorange shifting. Considering the asynchronous information from UAV’s LEO satellite navigation, inertial navigation, and ADS-B navigation, an information geometric probability model is constructed to unify the navigation information parameter format. Combined with factor graph theory, a collaborative positioning fusion framework is built to achieve rapid positioning of UAV clusters. Positioning tests are performed using China’s test satellites, and compared with existing cooperative positioning methods. The results show that the method proposed in this paper has improved positioning accuracy and ability to suppress drastic changes.</p>

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Information geometry aided UAV cluster cooperative positioning method with LEO satellite system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Chengkai Tang,
  • Wenbo Wang,
  • Lingling Zhang,
  • Zhe Yue,
  • Fahad Ghalib Abdulkadhim,
  • Hechen Lin

摘要

Low Earth Orbit (LEO) satellites are characterized by high received signal power and high signal propagation rate, and their pseudorange accuracy far exceeds traditional navigation constellations. However, the current LEO satellite’s beam coverage is small, resulting in a single UAV only able to receive 1–2 satellite beams, rendering positioning impossible. To address this issue, this paper proposes a cooperative positioning method for UAV clusters. This method uses the ranging information between UAV (Unmanned Aerial Vehicle) clusters for pseudorange shifting. Considering the asynchronous information from UAV’s LEO satellite navigation, inertial navigation, and ADS-B navigation, an information geometric probability model is constructed to unify the navigation information parameter format. Combined with factor graph theory, a collaborative positioning fusion framework is built to achieve rapid positioning of UAV clusters. Positioning tests are performed using China’s test satellites, and compared with existing cooperative positioning methods. The results show that the method proposed in this paper has improved positioning accuracy and ability to suppress drastic changes.