An efficient approach for multi-GNSS precise orbit determination using uncombined observations

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Abstract
Data processing of the Global Navigation Satellite System using uncombined (UC) observations has been a hot topic in recent years. However, the computation complexity and time consumption of the UC approach is much higher than those of the linear combination approach due to there being more parameters of interest. Concerning this issue, we propose a three-step approach by combining the ionosphere-free (IF) observation model and the UC observation model in a network scheme to reduce computation complexity. The first two steps are based on the IF observation model to detect cycle slips and gross errors using preprocessing and post-fit residual screening algorithms, respectively, while the third step is based on the UC observation model in which the ambiguities estimated from the IF model are used for UC ambiguity resolution (IF-IF-UC). To validate this approach, a network of 140 globally distributed stations is chosen and 1 year of GPS/GALILEO/BDS-3 dual-frequency data in 2021 is processed. The 1D RMSs of satellite orbit difference and the average STDs of satellite clock differences with respect to ESA’s products for GPS, Galileo and BDS-3 are 1.8, 2.4 and 4.1 cm and 0.024, 0.019 and 0.028 ns, respectively. Compared with the GIM products provided by the Center for Orbit Determination in Europe, the mean RMSs of the VTEC differences for GPS, Galileo and BDS-3 are 2.38, 2.30 and 3.09 TECU. For comparison purposes, the approach where all three-steps are based on the UC model is also conducted (UC-UC-UC). Results show that satellite orbits, clocks and VTECs from the IF-IF-UC approach are almost the same as those from the UC-UC-UC approach. By introducing high-performance linear algebra libraries, the elapsed time of the proposed IF-IF-UC approach is about 1 h, which is an improvement of 36% when compared to the UC-UC-UC approach. Nevertheless, the elapsed time is nearly 6 d when the UC-UC-UC approach is conducted without introducing high-performance linear algebra libraries.
Funding Information
  • National Natural Science Foundation of China (41931075)

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