 A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filterMingxing Chen, Zhi Xiong, Jun Xiong and Rong Wang International Journal of Distributed Sensor Networks, 2022, vol. 18, issue 1, 15501477211064758 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. Keywords: Cooperative navigation; factor graph; Kalman filter; UAV swarm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:18:y:2022:i:1:p:15501477211064758 DOI: 10.1177/15501477211064758 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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