Accuracy Comparison of GPT and SBAS Troposphere Models for GNSS Data Processing
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초록
The Global Navigation Satellite System (GNSS) signal gets delayed as it goes through the troposphere before reaching the GNSS antenna. Various tropospheric models are being used to correct the tropospheric delay. In this study, we compared effectiveness of two popular troposphere correction models: Global Pressure and Temperature (GPT) and Satellite-Based Augmentation System (SBAS). One-year data from a particular site was chosen as the test case. Tropospheric delays were computed using the GPT and SBAS models and compared with the International GNSS Service tropospheric product. The bias of SBAS model computations was 3.4 cm, which is four times lower than that of the GPT model. The cause of higher biases observed in the GPT model is the fact that one cannot get wet delays from the model. If SBAS-based wet delays are added to the hydrostatic delays computed using the GPT model, then the accuracy is similar to that of the full SBAS model. From this study, one can conclude that it is better to use the SBAS model than to use the GPT model in the standard code-pseudorange data processing.
Abstract
The Global Navigation Satellite System (GNSS) signal gets delayed as it goes through the troposphere before reaching the GNSS antenna. Various tropospheric models are being used to correct the tropospheric delay. In this study, we compared effectiveness of two popular troposphere correction models: Global Pressure and Temperature (GPT) and Satellite-Based Augmentation System (SBAS). One-year data from a particular site was chosen as the test case. Tropospheric delays were computed using the GPT and SBAS models and compared with the International GNSS Service tropospheric product. The bias of SBAS model computations was 3.4 cm, which is four times lower than that of the GPT model. The cause of higher biases observed in the GPT model is the fact that one cannot get wet delays from the model. If SBAS-based wet delays are added to the hydrostatic delays computed using the GPT model, then the accuracy is similar to that of the full SBAS model. From this study, one can conclude that it is better to use the SBAS model than to use the GPT model in the standard code-pseudorange data processing.
참고문헌 (9)
- Yoaz E. Bar-Sever, "Estimating horizontal gradients of tropospheric path delay with a single GPS receiver", Journal of Geophysical Research: Solid Earth, 103(B3), pp.5019~5035 (1998), American Geophysical Union (AGU)[DOI]
- J. Boehm, "Short Note: A global model of pressure and temperature for geodetic applications", Journal of Geodesy, 81(10), pp.679~683 (2007), Springer Nature[DOI]
- J. L. Davis, "Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length", Radio Science, 20(6), pp.1593~1607 (1985), American Geophysical Union (AGU)[DOI]
- Hofmann-Wellenhof, B., "GNSS Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more" (2008), Springer-Verlag
- H. S. Hopfield, "Two-quartic tropospheric refractivity profile for correcting satellite data", Journal of Geophysical Research, 74(18), pp.4487~4499 (1969), American Geophysical Union (AGU)[DOI]
- IERS Technical Note 36: IERS Conventions, "IERS", BKG, ~ (2010)
- Leick, A., "GPS satellite surveying" (2003), John Wiley and Sons, Inc.
- RTCA, "Minimum Operational Performance Standards for Global Positioning System", Satellite-Based Augmentation System Airborne Equipment, D0-229D, 2006
- J. Saastamoinen, "Contributions to the theory of atmospheric refraction", Bulletin géodésique, 107(1), pp.13~34 (1973), Springer Nature[DOI]