Undifferenced zenith tropospheric modeling and its application in fast ambiguity recovery for long-range network RTK reference stations
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A large number of continuously operating reference station (CORS) networks have been established around the world to support various high-precision navigation and positioning applications. However, the presence of significant tropospheric delays makes rapid ambiguity recovery for long inter-station baselines of network real-time kinematic (RTK) systems a major challenge. Since tropospheric delays are strongly temporally correlated over short periods, we propose an undifferenced (UD) zenith tropospheric prediction model to effectively correct tropospheric errors on the subsequent epoch measurements. Using 2-h sessions of the independent baselines in a CORS network, the ambiguities are easily and reliably resolved with the conventional ionospheric-free combination method. The derived double-differenced (DD), ionospheric-free residuals are then converted to UD residuals for each satellite and all stations. The UD residuals and the corresponding wet coefficients of each satellite are used to construct the zenith tropospheric model. The model is reconstructed every 5 min for each station. The slant tropospheric errors of observations within this period can be predicted using the established models. Seven independent baselines with an average length of 97 km are used to test the ambiguity recovery performance of the proposed method. The experimental results show that the proposed tropospheric prediction model can efficiently reduce the effects of slant tropospheric errors and improve the float solution of ambiguities. The average initialization time with the proposed method is less than 111.5 s, which is a 45% improvement with respect to the conventional approach. The proposed method was shown to be effective for fast ambiguity recovery of long-range baselines between reference stations.
KeywordsTropospheric errors Undifferenced Ambiguity resolution CORS
We are grateful to the anonymous reviewers and editors for their helpful suggestions and constructive comments, which have helped us significantly improve our paper. This work is partially supported by the National Science Fund for Distinguished Young Scholars (no. 41525014), National Natural Science Foundation of China (nos. 41704032, 41074008, 41431069, 41604028), Special Innovative Major Project of Hubei Province (no. 2018AAA066), Research Fund for the Doctoral Program of Higher Education of China (no. 20120141110025), Postdoctoral Science Foundation of China (no. 2018M642911), Anhui Natural Science Foundation (no. 1708085QD83), and Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (17-02-11).
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