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<p>PPP-RTK significantly reduces precise point positioning (PPP) convergence time by providing precise ionospheric information. Its performance depends on the accuracy of ionospheric products and the reliability of their associated model uncertainty (MU). This study focuses on low-latitude regions in China and evaluates the effectiveness of ionospheric products under station densities of 60&#xa0;km, 100&#xa0;km, 150&#xa0;km, and 200&#xa0;km (abbreviated as Nt1-4). Two ionospheric models, the low-order surface model (LSM) and the regional grid interpolation model (RGIM), were employed to generate ionospheric corrections, and their accuracy and associated model uncertainties were evaluated under both quiet and geomagnetically disturbed conditions. The mean accuracies of the LSMs under Nt1–Nt4 network densities were 0.67, 0.70, 0.78, and 0.93 TECU during quiet periods, compared with RGIM accuracies of 0.22, 0.32, 0.50, and 0.76 TECU, representing an average improvement of 44.1%. During disturbed periods, the accuracies of both models deteriorated, with LSM values ranging from 2.2 to 2.5 TECU and RGIM values from 1.0 to 2.1 TECU; neither showed significant correlation with elevation. The effectiveness of MU metrics varies with network configuration and ionospheric activity. For the LSM model, the model-based formal error MU (MU-F) outperforms the cross-validation-based MU (MU-C), whereas the opposite is observed for the RGIM model. In PPP-RTK convergence tests, RGIM-based corrections achieved the fastest convergence under quiet ionospheric conditions, with 95% of horizontal components reaching instantaneous convergence (at the first epoch) for Nt1–Nt3 network configurations. However, during disturbed periods, horizontal convergence times increased to an average of 5–9&#xa0;min. This delay is attributable not only to reduced ionospheric accuracy but also to the diminished reliability of MU-C and MU-F during geomagnetic storms. Application of MU-True, based on actual products errors, reduced convergence times by 39% (~ 2.7&#xa0;min), highlighting substantial potential for improving current MU indicators under disturbed ionospheric conditions.</p>

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Performance of a real-time PPP-RTK service under varying network densities and solar activity conditions

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Jun Tao,
  • Zhen Li,
  • Jie Zhang,
  • Wenlan Fan,
  • Yihao Jiang,
  • Qile Zhao

摘要

PPP-RTK significantly reduces precise point positioning (PPP) convergence time by providing precise ionospheric information. Its performance depends on the accuracy of ionospheric products and the reliability of their associated model uncertainty (MU). This study focuses on low-latitude regions in China and evaluates the effectiveness of ionospheric products under station densities of 60 km, 100 km, 150 km, and 200 km (abbreviated as Nt1-4). Two ionospheric models, the low-order surface model (LSM) and the regional grid interpolation model (RGIM), were employed to generate ionospheric corrections, and their accuracy and associated model uncertainties were evaluated under both quiet and geomagnetically disturbed conditions. The mean accuracies of the LSMs under Nt1–Nt4 network densities were 0.67, 0.70, 0.78, and 0.93 TECU during quiet periods, compared with RGIM accuracies of 0.22, 0.32, 0.50, and 0.76 TECU, representing an average improvement of 44.1%. During disturbed periods, the accuracies of both models deteriorated, with LSM values ranging from 2.2 to 2.5 TECU and RGIM values from 1.0 to 2.1 TECU; neither showed significant correlation with elevation. The effectiveness of MU metrics varies with network configuration and ionospheric activity. For the LSM model, the model-based formal error MU (MU-F) outperforms the cross-validation-based MU (MU-C), whereas the opposite is observed for the RGIM model. In PPP-RTK convergence tests, RGIM-based corrections achieved the fastest convergence under quiet ionospheric conditions, with 95% of horizontal components reaching instantaneous convergence (at the first epoch) for Nt1–Nt3 network configurations. However, during disturbed periods, horizontal convergence times increased to an average of 5–9 min. This delay is attributable not only to reduced ionospheric accuracy but also to the diminished reliability of MU-C and MU-F during geomagnetic storms. Application of MU-True, based on actual products errors, reduced convergence times by 39% (~ 2.7 min), highlighting substantial potential for improving current MU indicators under disturbed ionospheric conditions.