A New Optimal Subset Selection Method of Partial Ambiguity Resolution for Precise Point Positioning

Rapid and accurate ambiguity resolution is the core of high-precision precise point positioning (PPP) data processing. However, parameters in PPP observation models are easily affected by atmospheric residual gross errors, which lead to probability successfully fixing decreases computational burden increases full resolution. Therefore, an increasing number partial (PAR) strategies have been proposed. The selection optimal subset PAR crucial this method. traditional method commonly leads a single judgment criterion weakened geometric configuration strength because satellites with low elevation angles often eliminated during selection. In paper, multi-factor constrained for was proposed, incorporates variance, dilution precision (ADOP), satellite position (PDOP) ratio test values. order verify feasibility proposed method, tests under two schemes were performed GPS/Galileo based on static 15 Multi-GNSS Experiment (MGEX) tracking stations. results show that, compared variance sorting can further improve accuracy coordinate solution figure positioning. average root mean square residuals found decrease about 12.90%, 6.83% 9.39% eastern, northern vertical directions, respectively. increase fixed epoch rate ranged from 0.87% 33.33%, 8.71%.