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

Area of Science:

• Satellite Navigation
• Robotics
• Aerospace Engineering

Background:

• Low Earth Orbit (LEO) satellites offer high pseudorange accuracy but limited beam coverage, challenging Unmanned Aerial Vehicle (UAV) positioning.
• Single UAVs often receive insufficient LEO satellite signals (1-2 beams) for reliable navigation.
• Existing positioning methods struggle with the asynchronous and diverse data from LEO, inertial, and ADS-B navigation systems.

Purpose of the Study:

• To develop a cooperative positioning method for UAV clusters to overcome LEO satellite coverage limitations.
• To enhance the accuracy and robustness of UAV navigation in LEO environments.
• To unify and fuse asynchronous navigation data from multiple sources.

Main Methods:

• A cooperative positioning method utilizing inter-UAV ranging for pseudorange shifting.
• Construction of an information geometric probability model to unify asynchronous navigation data (LEO, inertial, ADS-B).
• Implementation of a collaborative positioning fusion framework based on factor graph theory.

Main Results:

• The proposed method significantly improved positioning accuracy for UAV clusters.
• Demonstrated enhanced ability to suppress drastic changes in positioning data.
• Validation through positioning tests using Chinese test satellites, outperforming existing cooperative methods.

Conclusions:

• The cooperative positioning method effectively addresses LEO satellite coverage limitations for UAV clusters.
• The information geometric model and factor graph fusion framework enable robust and accurate navigation.
• This approach represents a significant advancement in cooperative UAV positioning for LEO environments.