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<p>The real-time service (RTS) of providing precise satellite orbits and clocks is an important infrastructure for Global Navigation Satellite System (GNSS) precise positioning technologies. Filter-based precise orbit determination (POD) has been the promising trend in RTS for its superiority in terms of precision and stability. However, the heavy computational requirements and longer (re-)convergence time of filter-based POD limit its availability in some time-sensitive scenarios. In this study, we proposed a real-time integrated processing model to improve the performance of GNSS real-time orbits and clocks. Similar to the framework of filter-based POD, the proposed model integrates ground and LEO-onboard observations with square root information filter (SRIF). We collected real GNSS observation from eight LEO satellites to validate the proposed model. The results show that, by introducing onboard observations from eight LEO satellites, the convergence time, 3D RMS of GPS orbits and STD of GPS clocks can be improved by 30.4%, 46.9% and 37.5%, respectively, when using 30 globally distributed stations. When using the proposed model with ambiguity resolution (AR), the average 3D RMS of GPS orbits are 4.0 cm, which is improved by 27.8% compared to ground-only AR solutions. This accuracy is comparable to ground-only AR solutions with 90 stations, while improving computational efficiency by 66%. By using real-time products based on the proposed model, the positioning accuracy of GPS real-time kinematic PPP solutions can be improved by 27.7%, 30.9% and 28.5% in the east, north and up components, respectively, compared to that based on ground-only solutions with close computational performance.</p>

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Improving real-time GNSS orbits and clocks by filtered integrated processing multiple LEO onboard and ground observations

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • Hongjie Zheng,
  • Yongqiang Yuan,
  • Xingxing Li,
  • Keke Zhang

摘要

The real-time service (RTS) of providing precise satellite orbits and clocks is an important infrastructure for Global Navigation Satellite System (GNSS) precise positioning technologies. Filter-based precise orbit determination (POD) has been the promising trend in RTS for its superiority in terms of precision and stability. However, the heavy computational requirements and longer (re-)convergence time of filter-based POD limit its availability in some time-sensitive scenarios. In this study, we proposed a real-time integrated processing model to improve the performance of GNSS real-time orbits and clocks. Similar to the framework of filter-based POD, the proposed model integrates ground and LEO-onboard observations with square root information filter (SRIF). We collected real GNSS observation from eight LEO satellites to validate the proposed model. The results show that, by introducing onboard observations from eight LEO satellites, the convergence time, 3D RMS of GPS orbits and STD of GPS clocks can be improved by 30.4%, 46.9% and 37.5%, respectively, when using 30 globally distributed stations. When using the proposed model with ambiguity resolution (AR), the average 3D RMS of GPS orbits are 4.0 cm, which is improved by 27.8% compared to ground-only AR solutions. This accuracy is comparable to ground-only AR solutions with 90 stations, while improving computational efficiency by 66%. By using real-time products based on the proposed model, the positioning accuracy of GPS real-time kinematic PPP solutions can be improved by 27.7%, 30.9% and 28.5% in the east, north and up components, respectively, compared to that based on ground-only solutions with close computational performance.