An Adaptive Multiple Model Algorithm based on Factor Graph for Integrated Navigation System

To solve the problem of poor positioning accuracy or sensors failure of the vehicle navigation system caused by the multipath effect in urban environments, an adaptive multiple model estimation algorithm based on the factor graph is proposed. In this article, the navigation information of multiple models is fused with the robust factor graph algorithm to achieve global positioning optimization. The weight of information fusion by multiple models is adjusted through adaptive factors to reduce the impact of the multipath effect and satellite failure, which improves the positioning accuracy of the integrated vehicle navigation system. The experimental results show that the proposed algorithm can improve the positioning accuracy of vehicle navigation in complex urban environments. Compared with the traditional factor graph integrated navigation algorithm, the positioning accuracy of the proposed method in this paper is improved by more than 40%.