Hostname: page-component-745bb68f8f-v2bm5 Total loading time: 0 Render date: 2025-01-13T00:29:45.488Z Has data issue: false hasContentIssue false
  • English
  • Français

Improvement of Multi-GNSS Precise Point Positioning Performances with Real Meteorological Data

Published online by Cambridge University Press:  12 July 2018

Ke Su  and
Shuanggen Jin
Ke Su
Affiliation:
(Shanghai Key Laboratory of Space Navigation and Positioning Technology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China) (University of Chinese Academy of Sciences, Beijing 100049, China)
Shuanggen Jin*
Affiliation:
(Shanghai Key Laboratory of Space Navigation and Positioning Technology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China) (School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Tropospheric delay is one of the main error sources in Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP). Zenith Hydrostatic Delay (ZHD) accounts for 90% of the total delay. This research focuses on the improvements of ZHD from tropospheric models and real meteorological data on the PPP solution. Multi-GNSS PPP experiments are conducted using the datasets collected at Multi-GNSS Experiments (MGEX) network stations. The results show that the positioning accuracy of different GNSS PPP solutions using the meteorological data for ZHD correction can achieve an accuracy level of several millimetres. The average convergence time of a PPP solution for the BeiDou System (BDS), the Global Positioning System (GPS), Global Navigation Satellite System of Russia (GLONASS), BDS+GPS, and BDS+GPS+GLONASS+Galileo are 55·89 min, 25·88 min, 33·30 min, 20·50 min and 15·71 min, respectively. The results also show that atmospheric parameters provided by real meteorological data have little effect on the horizontal components of positioning compared to the meteorological model, while in the vertical component, the positioning accuracy is improved by 90·6%, 33·0%, 22·2% and 19·8% compared with the standard atmospheric model, University of New Brunswick (UNB3m) model, Global Pressure and Temperature (GPT) model, and Global Pressure and Temperature-2 (GPT2) model and the convergence times are decreased 51·2%, 32·8%, 32·5%, and 32·3%, respectively.

Keywords

GNSSPPPTropospheric delayMeteorology
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 6 , November 2018 , pp. 1363 - 1380
Copyright
Copyright © The Royal Institute of Navigation 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Böhm, J., Heinkelmann, R. and Schuh, H. (2007). Short note: a global model of pressure and temperature for geodetic applications. Journal of Geodesy, 81(10), 679–683.Google Scholar
Cai, C., Gao, Y., Pan, L. and Zhu, J. (2015). Precise point positioning with quad-constellations: GPS, BeiDou, GLONASS and Galileo. Advances in Space Research, 56(1), 133–143.Google Scholar
Collins, J. P. and Langley, R. B. (1997). A tropospheric delay model for the user of the wide area augmentation system. Department of Geodesy and Geomatics Engineering, University of New Brunswick.Google Scholar
Collins, J. P. and Langley, R. B. (1999). Nominal and extreme error performance of the UNB3 tropospheric delay model. Department of Geodesy and Geomatics Engineering, University of New Brunswick, 173pp.Google Scholar
Ding, W., Teferle, F. N., Kazmierski, K., Laurichesse, D. and Yuan, Y. (2017). An evaluation of real-time troposphere estimation based on GNSS Precise Point Positioning. Journal of Geophysical Research: Atmospheres, 122(5), 2779–2790.Google Scholar
Hadas, T., Kaplon, J., Bosy, J., Sierny, J. and Wilgan, K. (2013). Near-real-time regional troposphere models for the GNSS precise point positioning technique. Measurement Science and Technology, 24(5), 055003.Google Scholar
Hadas, T., Teferle, F. N., Kazmierski, K., Hordyniec, P. and Bosy, J. (2017). Optimum stochastic modeling for GNSS tropospheric delay estimation in real-time. GPS Solutions, 21(3), 1069–1081.Google Scholar
Hopfield, H.S. (1969). Two-quartic tropospheric refractivity profile for correcting satellite data. Journal of Geophysical Research, 74(18), 4487–4499.Google Scholar
Jin, S.G., Han, L. and Cho, J. (2011). Lower atmospheric anomalies following the 2008 Wenchuan Earthquake observed by GPS measurements. Journal of Atmospheric and Solar-Terrestrial Physics, 73(7–8), 810–814, doi: 10.1016/j.jastp.2011.01.023.Google Scholar
Jin, S.G., Li, Z.C. and Cho, J.H. (2008). Integrated water vapor field and multi-scale variations over China from GPS measurements. Journal of Applied Meteorology and Climatology, 47(11), 3008–3015, doi: 10.1175/2008JAMC1920.1.Google Scholar
Jin, S.G., Luo, O.F. and Gleason, S. (2009). Characterization of diurnal cycles in ZTD from a decade of global GPS observations. Journal of Geodesy, 83(6), 537–545, doi: 10.1007/s00190-008-0264-3.Google Scholar
Jin, S.G., Luo, O.F. and Ren, C. (2010). Effects of physical correlations on long-distance GPS positioning and zenith tropospheric delay estimates. Advances in Space Research, 46(2), 190–195, doi: 10.1016/j.asr.2010.01.017.Google Scholar
Jin, S.G., and Park, P.H. (2006). Strain accumulation in South Korea inferred from GPS measurements. Earth, Planets and Space, 58(5), 529–534, doi: 10.1186/BF03351950.Google Scholar
Jin, S.G., Wang, J., Zhang, H. and Zhu, W.Y. (2004). Real-time monitoring and prediction of the total ionospheric electron content by means of GPS observations, Chinese Astronomy and Astrophysics, 28(3), 331–337, doi: 10.1016/j.chinastron.2004.07.008.Google Scholar
Kouba, J. (2009). Testing of global pressure/temperature (GPT) model and global mapping function (GMF) in GPS analyses. Journal of Geodesy, 83(3), 199–208.Google Scholar
Lagler, K., Schindelegger, M., Böhm, J., Krásná, H. and Nilsson, T. (2013). GPT2: Empirical slant delay model for radio space geodetic techniques. Geophysical research letters, 40(6), 1069–1073.Google Scholar
Leandro, R., Santos, M. C. and Langley, R. B. (2006). UNB neutral atmosphere models: development and performance. In Proceedings of ION NTM. 2006, 52(1), 564–73.Google Scholar
Leandro, R. F., Langley, R. B. and Santos, M. C. (2008). UNB3m_pack: a neutral atmosphere delay package for radiometric space techniques. GPS Solutions, 12(1), 65–70.Google Scholar
Rizos, C., Montenbruck, O., Weber, R., Weber, G., Neilan, R. and Hugentobler, U. (2013). The IGS MGEX experiment as a milestone for a comprehensive multi-GNSS service. In: Proceedings of the ION 2013 Pacific PNT Meeting (ION-PNT-2013), April 23–25, Honolulu, Hawaii, USA, 289–295.Google Scholar
Saastamoinen, J. (1972). Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. Use of Artificial Satellites for Geodesy, 15(6), 247–251.Google Scholar
Steigenberger, P., Boehm, J. and Tesmer, V. (2009). Comparison of GMF/GPT with VMF1/ECMWF and implications for atmospheric loading. Journal of Geodesy, 83(10), 943–951.Google Scholar
Swanson, G. S. and Trenberth, K. E. (1981). Trends in the Southern Hemisphere tropospheric circulation. Monthly Weather Review, 109(9), 1879–1889.Google Scholar
Tenzer, R., Chen, W., Tsoulis, D., Bagherbandi, M., Sjoberg, L., Novak, P. and Jin S.G. (2015). Analysis of the refined CRUST1.0 crustal model and its gravity field. Survey Geophysics, 36(1), 139–165, doi: 10.1007/s10712-014-9299-6.Google Scholar
Trenberth, K. E. (1981). Seasonal variations in global sea level pressure and the total mass of the atmosphere. Journal of Geophysical Research: Oceans, 86(C6), 5238–5246.Google Scholar
Witchayangkoon, B. (2000). Elements of GPS Precise Point Positioning. Ph.D. dissertation, Department of Spatial Information Science and Engineering, University of Maine, Orono, Maine, U.S.A.Google Scholar
Yao, Y., He, C., Zhang, B. and Xu, C. (2013). A new global zenith tropospheric delay model GZTD. Chinese Journal of Geophysics-Chinese Edition, 56(7), 2218–2227.Google Scholar
Zhang, H., Yuan, Y., Li, W., Li, Y. and Chai, Y. (2016). Assessment of three tropospheric delay models (IGGTROP, EGNOS and UNB3M) based on precise point positioning in the Chinese region. Sensors, 16(1), 122.Google Scholar
Zumberge, J. F., Heflin, M. B., Jefferson, D. C., Watkins, M. M. and Webb, F. H. (1997). Precise point positioning for the efficient and robust analysis of GPS data from large networks. Journal of Geophysical Research: Solid Earth, 102(B3), 5005–5017.Google Scholar