FCB Estimation Using IGS Real-Time Products and Its Application in Precise Point Positioning

  • Bo Jiao
  • Yishuai Shi
  • Jinming Hao
  • Cheng Fang
  • Xufeng Wen
  • Baofeng Song
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 498)


Real-time capability , positioning accuracy and convergence time ar e the keys to the performance of Precise Point Positioning (PPP). In recent years, the development of IGS Real-Time Services (RTS) has improved the performance of real-time PPP to a large extent. Meanwhile, the PPP ambiguity resolution technique, which significantly improves the positioning accuracy and convergence time, has become mature gradually. If the advantages of both techniques can be combined to realize real-time PPP with ambiguity resolution, the application of PPP would be greatly expanded. In this paper, a real-time satellite-satellite single-difference fractional cycle bias (FCB) estimation method based on RTS products is proposed. To meet the demand of real-time PPP, narrow-lane FCBs are forecasted by short-term linear extrapolation. Taking accuracy levels and data amount into consideration, the forecasting interval for real-time wide-lane and narrow-lane FCBs are determined. Finally, the performance of real-time PPP with ambiguity resolution using real-time FCBs is evaluated in both static and kinematic modes. The results show that wide-lane FCBs to be forecasted daily and narrow-lane FCBs to be forecasted every 5 min can satisfy the need of real-time PPP with ambiguity resolution. In static mode, the average time to first fix (TTFF) is 15.97 min, and the average precision of the north, east and up components for hourly solution are 1.51, 1.64 and 2.18 cm respectively. As for kinematic mode, an average TTFF of 24.36 min is needed, and the 3D position error in fixed solution is decreased by 42.59% compared with float solution.


Precise point positioning Fractional cycle bias Ambiguity resolution Real-time solution Least square fit 



This research work was supported by the Natural Science Foundation of China (41604032).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Bo Jiao
    • 1
  • Yishuai Shi
    • 1
  • Jinming Hao
    • 1
  • Cheng Fang
    • 1
  • Xufeng Wen
    • 1
  • Baofeng Song
    • 1
  1. 1.Information Engineering UniversityZhengzhouChina

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