Machine learning-based global positioning performance evaluation method of satellite navigation system

For observation data such as pseudorange phase and broadcast ephemeris of satellite navigation systems, this paper adopts technical means such as feature extraction and model regression to find the intrinsic characteristics of the data from two dimensions of data type and observation time, excavate the feature associations between massive station data, and use machine learning methods to evaluate the global positioning performance of satellite navigation systems. The evaluation method proposed in this article has been validated on actual station data. The average positioning accuracy of 12 station models in China and surrounding areas, 1−MAPE, is 92.36%, with the worst being PTGG stations and 1−MAPE being 89.26%. The average positioning accuracy of 120 station models worldwide, 1−MAPE, is 86.59%, the worst being SCOR stations and 1−MAPE being 81.46%, which is in good agreement with the measured values obtained under the traditional mathematical statistical framework, It is shown that the method for evaluating satellite navigation and positioning performance based on machine learning models is feasible and effective. Machine learning models have strong evaluation capabilities and high generalization in big data statistical analysis, breaking through the current global positioning performance evaluation approach that only uses traditional mathematical statistics.