In the process of unmanned aerial vehicle(UAV) positioning system fusion processing multiple different frequency and nonlinear sensor transmission signal，in order to solve the problems of low positioning accuracy, poor disturbance immunity and weak fault tolerance in the conventional information fusion method based on factor graph model, a multi-sensor fusion localization algorithm based on the factor graph optimized by elimination algorithm is proposed. In order to improve the anti-interference and fault tolerance of the proposed new algorithm, a sliding window is added to the chain factor graph model to retain the historical state information in the window. At the same time, for the purpose of avoiding high-dimensional matrix operation, the elimination algorithm is introduced to transform the factor graph into a Bayesian network and marginalize the historical state to realize matrix dimension reduction. In the comparison experiments, the UAV positioning system adopts the conventional factor graph algorithm and the multi-sensor fusion positioning algorithm based on the factor graph optimized by elimination algorithm respectively. The experimental results reveal that the proposed algorithm can significantly improve the accuracy and reliability of positioning, and greatly reduce the amount of calculation of information fusion.