Undifferenced zenith tropospheric modeling and its application in fast ambiguity recovery for long-range network RTK reference stations

  • 发布时间:[2020-04-30] 来源:[学院] 点击量:[1345]
作者: Chen, DZ (Chen, Dezhong); Ye, SR (Ye, Shirong); Xu, CJ (Xu, Caijun); Jiang, WP (Jiang, Weiping); Jiang, P (Jiang, Peng); Chen, H (Chen, Hua)
来源出版物: GPS SOLUTIONS  : 23  : 1  文献号: UNSP 26  DOI: 10.1007/s10291-018-0815-x  出版年: JAN 2019
摘要: 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 5min 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 97km 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 111.5s, 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.

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