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An approach to GPS clock prediction for real-time PPP during outages of RTS stream

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The international GNSS service (IGS) has been providing an open-access real-time service (RTS) since 2013, which allows users to carry out real-time precise point positioning (RT-PPP). As the availability of RTS products is vital for RT-PPP, a disruption in receiving RTS products will be a concern. Currently, the IGS Ultra-rapid (IGU) orbit is accurate enough to be used as an alternative orbit for RTS during RTS outages, while the precision of the IGU predicted clock offsets is far below that of the RTS clock product. The existing clock prediction methods based on received RTS clock data will not work well if the discontinuity arises shortly after the start of the RT-PPP processing due to the lack of RTS clock data to fit the prediction model or to predict clock offsets at a high precision. Even if there is a sufficient amount of RTS clock data available, saving large amounts of RTS clock data would also use processor memory. An alternate approach for GPS clock prediction is proposed. The prediction model, composed of linear polynomial and sinusoidal terms, is similar to those used by the precious methods. The main innovation is the determination of the model coefficients: coefficients of linear and sinusoidal terms are estimated with the epoch-differenced clock offsets from the IGU observed part, while the constant coefficient is computed with the latest RTS clock corrections. There is no need to save the received RTS clock corrections, and clock prediction can be carried out even with only one epoch of RTS data. Evaluation of the proposed method shows that the predicted clock offsets within a short period of prediction time, e.g., 5 min, are slightly worse than RTS clock data. Even when the predicted time reaches up to 1 h, the precision of the predicted clock offsets is still higher than that of IGU predicted clock offsets by about 50%.

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This study was supported by Key Program of National Natural Science Foundation of China (Grant No.: 41631073) and National Natural Science Foundation of China (Grant No.: 41374008). The authors also really acknowledge to the IGS for providing GNSS data and real-time products. Our deepest gratitude also goes to anonymous reviewers for their careful work and thoughtful suggestions.

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Correspondence to Zhixi Nie.

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Nie, Z., Gao, Y., Wang, Z. et al. An approach to GPS clock prediction for real-time PPP during outages of RTS stream. GPS Solut 22, 14 (2018).

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