Skip to main content Accessibility help
×
Home
Hostname: page-component-78bd46657c-lfkwv Total loading time: 0.284 Render date: 2021-05-09T00:49:40.622Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

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

Published online by Cambridge University Press:  12 July 2018

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)
Corresponding
E-mail address:

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.

Type
Research Article
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.

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), 679683.CrossRefGoogle 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), 133143.CrossRefGoogle 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), 27792790.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.CrossRefGoogle 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), 10691081.CrossRefGoogle Scholar
Hopfield, H.S. (1969). Two-quartic tropospheric refractivity profile for correcting satellite data. Journal of Geophysical Research, 74(18), 44874499.CrossRefGoogle 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), 810814, doi: 10.1016/j.jastp.2011.01.023.CrossRefGoogle 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), 30083015, doi: 10.1175/2008JAMC1920.1.CrossRefGoogle 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), 537545, doi: 10.1007/s00190-008-0264-3.CrossRefGoogle 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), 190195, doi: 10.1016/j.asr.2010.01.017.CrossRefGoogle Scholar
Jin, S.G., and Park, P.H. (2006). Strain accumulation in South Korea inferred from GPS measurements. Earth, Planets and Space, 58(5), 529534, doi: 10.1186/BF03351950.CrossRefGoogle 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), 331337, 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), 199208.CrossRefGoogle 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), 10691073.CrossRefGoogle ScholarPubMed
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), 6570.CrossRefGoogle 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, 289295.Google Scholar
Saastamoinen, J. (1972). Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. Use of Artificial Satellites for Geodesy, 15(6), 247251.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), 943951.CrossRefGoogle Scholar
Swanson, G. S. and Trenberth, K. E. (1981). Trends in the Southern Hemisphere tropospheric circulation. Monthly Weather Review, 109(9), 18791889.2.0.CO;2>CrossRefGoogle 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), 139165, doi: 10.1007/s10712-014-9299-6.CrossRefGoogle 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), 52385246.CrossRefGoogle 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), 22182227.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.CrossRefGoogle ScholarPubMed
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), 50055017.CrossRefGoogle Scholar

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

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

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

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

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *