LI Qin, YAO Wanqiang, TU Rui. Application of Helmert variance component estimation in GPS/GLONASS/BDS/Galileo combined precise point positioning weight determination[J]. GNSS World of China, 2022, 47(3): 16-24. DOI: 10.12265/j.gnss.2022047
Citation: LI Qin, YAO Wanqiang, TU Rui. Application of Helmert variance component estimation in GPS/GLONASS/BDS/Galileo combined precise point positioning weight determination[J]. GNSS World of China, 2022, 47(3): 16-24. DOI: 10.12265/j.gnss.2022047

Application of Helmert variance component estimation in GPS/GLONASS/BDS/Galileo combined precise point positioning weight determination

  • Multi-constellation combined positioning can improve the performance of navigation and positioning, but a suitable stochastic model needs to be considered when combining observations from different constellations. The traditional method is to directly set the equivalent weight of each system based on experience, which will lead to inaccurate determination of the stochastic model, and thus affect the performance improvement of the combined system. In this paper, Helmert variance component estimation method is applied to GPS/GLONASS/BDS/Galileo combined precise point positioning to adaptively determine the weight ratio between systems. The static and pseudo-dynamic tests were carried out using the daily observation dataset collected at 10 stations in the global International GNSS Service (IGS) Multi-GNSS experiment (MGEX) observation network over one week of February 8 to February 14, 2021. The results show that the Helmert variance component estimation weighting method can significantly improve the convergence speed of GPS/GLONASS/BDS/Galileo combined precise point positioning (PPP), with an average increase of 52% in static mode and 64% in pseudo-dynamic mode. Because the positioning accuracy is mainly determined by the carrier phase observation accuracy and error correction level, the Helmert variance component estimation method has no obvious improvement on positioning accuracy in static and pseudo-dynamic tests.
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