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Session D1: Space Navigation and Observation

Autonomous Navigation of Satellite via Intelligent Factor Graphs Theory
Bing Xiao, Xiwei Wu, Cihang Wu, lingwei Li, Northwestern Polytechnical University, China
Location: Galleria I/II
Alternate Number 2

High-precision navigation is an inevitable requirement for the successful operation of satellites. This paper proposes a new method based on the intelligent factor graph for multi-sensor information fusion to realize satellite navigation. This method introduces intelligent factor graph theory and Bayesian tree theory to design an incremental smoothing optimization algorithm based on the factor graph. The proposed method allows multi-rate, asynchronous, and possibly delayed measurements to be incorporated in a natural way. Applying this method, the autonomous navigation accuracy can be significantly improved. Moreover, it does not have a dependence on infrastructure and saves navigation costs. In addition, the Intelligent factor graph algorithm is essentially a process of factorization. It is proposed to represent the probability graph model as a bipartite graph, which can achieve dimensionality reduction, significantly reduce computational complexity and achieve accurate satellite autonomous navigation.

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