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A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

Author

Listed:
  • Mingxing Chen
  • Zhi Xiong
  • Jun Xiong
  • Rong Wang

Abstract

Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.

Suggested Citation

  • Mingxing Chen & Zhi Xiong & Jun Xiong & Rong Wang, 2022. "A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter," International Journal of Distributed Sensor Networks, , vol. 18(1), pages 15501477211, January.
    • Handle: RePEc:sae:intdis:v:18:y:2022:i:1:p:15501477211064758
    DOI: 10.1177/15501477211064758
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