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<p>Stochastic models play an important role in accurate global navigation satellite system (GNSS) data processing. The traditional weighting strategies based on satellite elevation and the carrier-to-noise ratio cannot optimally represent the contributions of observations between stations and satellites in network data processing. In this analysis, an improved time-varying stochastic model considering satellite-dependent and station-dependent accuracy factors is proposed for real-time (RT) GPS/BDS satellite clock offset estimation. The analysis results obtained for the estimated accuracy factors suggest that the values between stations and satellites are very different and thus have to be considered in network data processing. Compared with those of the traditional method, the accuracies of the RT satellite clock offsets are improved by 39.8%, 43.3%, and 28.5% for the GPS, BDS IGSO, and BDS MEO satellites, respectively, when hourly updated GNSS orbits are utilized. The accuracy improvements of the RT satellite clock offsets decrease when postprocessed GNSS orbits with higher accuracies are utilized. The convergence speed in RT kinematic precise point positioning (PPP) is not accelerated by the more precise RT satellite clock offsets, and it becomes slightly slower with additional satellite-dependent accuracy factors. The positioning accuracies of the vertical component improve from 6.49&#xa0;cm, 11.35&#xa0;cm, and 5.92&#xa0;cm to 4.85&#xa0;cm, 9.03&#xa0;cm, and 3.88&#xa0;cm in the GPS, BDS, and GPS+BDS solutions, respectively, when the hourly updated satellite orbits are imported. The positioning accuracies decrease without importing satellite-dependent accuracy factors into RT kinematic PPP but still improve due to the more precise calculated satellite clock offsets. All these results suggest that the proposed model is beneficial for improving the performance attained in RT satellite clock offset estimation and the subsequent RT kinematic PPP.</p>

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Analysis of an improved time-varying stochastic model for real-time GPS/BDS satellite clock offset estimation

  • 期刊论文

  • 发表日期：

    2025年6月10日

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  • Wenwu Ding,
  • Pengfei Zhang,
  • Yunbin Yuan,
  • Haitao Wang

摘要

Stochastic models play an important role in accurate global navigation satellite system (GNSS) data processing. The traditional weighting strategies based on satellite elevation and the carrier-to-noise ratio cannot optimally represent the contributions of observations between stations and satellites in network data processing. In this analysis, an improved time-varying stochastic model considering satellite-dependent and station-dependent accuracy factors is proposed for real-time (RT) GPS/BDS satellite clock offset estimation. The analysis results obtained for the estimated accuracy factors suggest that the values between stations and satellites are very different and thus have to be considered in network data processing. Compared with those of the traditional method, the accuracies of the RT satellite clock offsets are improved by 39.8%, 43.3%, and 28.5% for the GPS, BDS IGSO, and BDS MEO satellites, respectively, when hourly updated GNSS orbits are utilized. The accuracy improvements of the RT satellite clock offsets decrease when postprocessed GNSS orbits with higher accuracies are utilized. The convergence speed in RT kinematic precise point positioning (PPP) is not accelerated by the more precise RT satellite clock offsets, and it becomes slightly slower with additional satellite-dependent accuracy factors. The positioning accuracies of the vertical component improve from 6.49 cm, 11.35 cm, and 5.92 cm to 4.85 cm, 9.03 cm, and 3.88 cm in the GPS, BDS, and GPS+BDS solutions, respectively, when the hourly updated satellite orbits are imported. The positioning accuracies decrease without importing satellite-dependent accuracy factors into RT kinematic PPP but still improve due to the more precise calculated satellite clock offsets. All these results suggest that the proposed model is beneficial for improving the performance attained in RT satellite clock offset estimation and the subsequent RT kinematic PPP.