Estimating and assessing Galileo navigation system satellite and receiver differential code biases using the ionospheric parameter and differential code bias joint estimation approach with multi-GNSS observations

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Abstract

With the increased number of Galileo navigation satellites joining the Global Navigation Satellite Systems (GNSS) service, there is a strong need for estimating their differential code biases (DCBs) for high-precision GNSS applications. There have been studies for estimating DCBs based on an external global ionospheric model (GIM) proposed by Montenbruck et al. (2014). In this study, we take a different approach by joining the construction of a GIM and estimating DCB together with multi-GNSS observations, including GPS, the BeiDou navigation system, and the Galileo navigation system (GAL). This approach takes full advantage of the collective strength of the individual systems while maintaining high solution consistency. Daily GAL DCBs were estimated simultaneously with ionospheric model parameters from 3 months' multi-GNSS observations. The stability of the resulting GAL DCB estimates was analyzed in detail. It was found that the standard deviations (STDs) of all satellite DCBs were less than 0.17 ns. For GAL receivers, the STDs were greater than for the satellites, with most values <2 ns. Comparison of the statistics of time-ranged stability of satellite DCBs over different time intervals revealed that the difference in STD between 28 and 7 day intervals was small, with the maximum not exceeding 0.01 ns. In almost all cases, the difference in GAL satellite DCBs between two consecutive days was <0.8 ns. The main conclusion is that based on the stability of the GAL DCBs, only occasional calibration is required. Furthermore, the 30 day-averaged satellite DCBs may satisfy the requirement of high-precision applications depending on the GAL satellite DCBs.

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Xue, J., Song, S., Liao, X., & Zhu, W. (2016). Estimating and assessing Galileo navigation system satellite and receiver differential code biases using the ionospheric parameter and differential code bias joint estimation approach with multi-GNSS observations. Radio Science, 51(4), 271–283. https://doi.org/10.1002/2015RS005797

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