Dual dynamic carrier positioning algorithm based on double factor graph and ambiguity optimization

With the rapid development of unmanned systems, autonomous driving, and other related fields, dual dynamic carrier relative positioning technology has become increasingly important for achieving high-precision real-time positioning and adapting to complex environments. To improve the accuracy and reliability of the existing relative positioning algorithm for dual dynamic carrier, this paper proposes a dual dynamic carrier relative positioning algorithm based on Double Factor graph and Ambiguity Resolution optimization (DF-AR), incorporating reference station processing, ambiguity resolution, and mobile station resolution optimization methods. To improve relative positioning accuracy, a factor graph optimization model integrating multi-frequency and multi-system Kalman filtering is used to suppress single-point positioning errors at the reference station. Using baseline constraints along with data quality weighting, an improved data and model-driven partial ambiguity resolution strategy is constructed. The ambiguity subset with higher reliability is selected to improve the success rate of ambiguity fixation and the reliability of the relative positioning solution. Based on these improvements, a sliding window is introduced in the factor graph optimization model to dynamically adjust the data amount. The positioning solution of the mobile station is reoptimized to achieve more robust relative positioning results. Static evaluation experiments, dual-vehicle and UAV/vehicle dynamic relative positioning experiments were carried out. The experimental results show that in different experimental scenarios, the baseline solution error of the DF-AR relative positioning algorithm has an error reduction of 69.72%, 94.89%, and 68.03% compared to the RTKLIB algorithm. The baseline solution accuracy has been improved from meter level to decimeter level, effectively enhancing the reliability and accuracy of relative positioning.