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<p>In Network Real-Time Kinematic (NRTK) high-precision positioning, rapid and reliable ambiguity resolution relies on high-precision ionospheric modeling. However, under current NRTK frameworks, ionospheric modeling depends solely on Continuously Operating Reference Stations (CORS), leading to degraded positioning performance in scenarios with sparse reference station coverage or active ionospheric conditions. With the expansion of NRTK services from surveying applications to broader mass-market applications, intelligent devices such as unmanned aerial vehicles and autonomous vehicles are increasingly integrated into NRTK systems. Leveraging the observation data from selected terminals could enhance the quality of differential corrections and improve NRTK positioning performance. In this study, we propose a platform-terminal interactive NRTK positioning method. The method first transmits ionospheric delay information resolved at terminals by a geometry-free model back to the NRTK platform. Subsequently, the NRTK platform integrates ionospheric delays from virtual reference stations (VRS) and reference station baselines to reconstruct the terminal ionospheric delays into a unified reference framework, and performs appropriate data quality control. Finally, an ionospheric model that considers the relative positions between terminals and reference stations is employed to generate ionospheric delay corrections for the VRS, which is then broadcast to terminals for positioning. Experimental validation was conducted using two AUSCORS network cell datasets, whose average baseline lengths were 107.9 and 141.9&#xa0;km, respectively, to evaluate the ionospheric modeling and positioning performance of the proposed method. Experimental results indicate that the Linear Interpolation Model (LIM) and Low-order Surface Model (LSM) improve ionospheric modeling accuracy by 37.9 and 40.2%, respectively, compared to conventional NRTK. In terms of positioning accuracy, they achieve improvements of 56.6 and 59.4%, with the fixing rate increasing by 10.0 and 10.1%, respectively. These experimental results demonstrate the feasibility and performance advantages of the proposed method, which provides potential for high-precision NRTK positioning.</p>

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Platform-terminal interactive VRS observation generation method for GNSS network RTK positioning

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Wang Gao,
  • Hao Liu,
  • Shuguo Pan,
  • Zeyu Zhang

摘要

In Network Real-Time Kinematic (NRTK) high-precision positioning, rapid and reliable ambiguity resolution relies on high-precision ionospheric modeling. However, under current NRTK frameworks, ionospheric modeling depends solely on Continuously Operating Reference Stations (CORS), leading to degraded positioning performance in scenarios with sparse reference station coverage or active ionospheric conditions. With the expansion of NRTK services from surveying applications to broader mass-market applications, intelligent devices such as unmanned aerial vehicles and autonomous vehicles are increasingly integrated into NRTK systems. Leveraging the observation data from selected terminals could enhance the quality of differential corrections and improve NRTK positioning performance. In this study, we propose a platform-terminal interactive NRTK positioning method. The method first transmits ionospheric delay information resolved at terminals by a geometry-free model back to the NRTK platform. Subsequently, the NRTK platform integrates ionospheric delays from virtual reference stations (VRS) and reference station baselines to reconstruct the terminal ionospheric delays into a unified reference framework, and performs appropriate data quality control. Finally, an ionospheric model that considers the relative positions between terminals and reference stations is employed to generate ionospheric delay corrections for the VRS, which is then broadcast to terminals for positioning. Experimental validation was conducted using two AUSCORS network cell datasets, whose average baseline lengths were 107.9 and 141.9 km, respectively, to evaluate the ionospheric modeling and positioning performance of the proposed method. Experimental results indicate that the Linear Interpolation Model (LIM) and Low-order Surface Model (LSM) improve ionospheric modeling accuracy by 37.9 and 40.2%, respectively, compared to conventional NRTK. In terms of positioning accuracy, they achieve improvements of 56.6 and 59.4%, with the fixing rate increasing by 10.0 and 10.1%, respectively. These experimental results demonstrate the feasibility and performance advantages of the proposed method, which provides potential for high-precision NRTK positioning.