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A Multi-Station Troposphere Modelling Method Based on Error Compensation Considering the Influence of Height Factor

Published online by Cambridge University Press:  23 July 2020

Qing Zhao Open the ORCID record for Qing Zhao [Opens in a new window] ,
Shuguo Pan Open the ORCID record for Shuguo Pan [Opens in a new window] ,
Chengfa Gao ,
Longlei Qiao ,
Wang Gao ,
Ruicheng Zhang  and
Guoliang Liu
Qing Zhao
Affiliation:
(School of Transportation, Southeast University, Nanjing, China)
Shuguo Pan*
Affiliation:
(School of Instrument Science and Engineering, Southeast University, Nanjing, China) (Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing, China)
Chengfa Gao
Affiliation:
(School of Transportation, Southeast University, Nanjing, China)
Longlei Qiao
Affiliation:
(Nanjing Compass Navigation Technology Company Limited, Nanjing, China)
Wang Gao*
Affiliation:
(School of Instrument Science and Engineering, Southeast University, Nanjing, China) (Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing, China)
Ruicheng Zhang
Affiliation:
(School of Transportation, Southeast University, Nanjing, China)
Guoliang Liu
Affiliation:
(School of Instrument Science and Engineering, Southeast University, Nanjing, China) (Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing, China)
*
(E-mail: [email protected]; [email protected])
(E-mail: [email protected]; [email protected])
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Abstract

One critical issue in network real-time kinematic (NRTK) is the interpolation of atmospheric delay for user stations. Some classic interpolation algorithms, such as linear interpolation method (LIM), ignore the strong correlation between tropospheric delay and height factors, and the interpolation accuracy is poor in areas with large height difference. To solve this problem, a troposphere modelling method based on error compensation, namely ECDIM (Error Compensation-Based DIM), is proposed, and this method can be applied to both conventional single Delaunay triangulated network (DTN) and multi-station scenarios. The results of California Real Time Network (CRTN) with large height difference show that compared with LIM, the overall modelling accuracy with ECDIM has been improved by 50.1% to 67.3%, and especially for low elevation satellites (e.g., 10–20 degree), the accuracy is increased from tens of centimetres to a few centimetres. At user end, the positioning error in up direction with LIM has an obvious systematic deviation, and the fix rate of epoch is relatively low. This situation has been improved significantly after using ECDIM. The results of Tianjin Continuously Operating Reference System (TJCORS) show that in areas with small height difference, both methods have achieved high precision interpolation accuracy, and the positioning accuracy with ECDIM in up direction is improved by 21.2% compared with LIM.

Keywords

Network Real-Time Kinematic (NRTK)Troposphere ModellingError CompensationMulti-StationHeight FactorPositioning Accuracy
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 38 - 59
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Copyright
Copyright © The Royal Institute of Navigation 2020

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References

REFERENCES

Al-Shaery, A., Lim, S.and Rizos, C. (2011). Investigation of different interpolation models used in Network-RTK for the virtual reference station technique. Journal of Global Positioning Systems, 10(2), 136–148.CrossRefGoogle Scholar
Aponte, J., Meng, X., Moore, T., Hill, C.and Burbidge, M. (2008). Evaluating The Performance of NRTK GPS Positioning for Land Navigation Applications. Royal Institute of Navigation NAV08 and International Loran Association ILA37. Church House, Westminster, London.Google Scholar
Böhm, J., Möller, G., Schindelegger, M., Pain, G.and Weber, R. (2015). Development of an improved empirical model for slant delays in the troposphere (GPT2w). GPS Solutions, 19(3), 433–441.CrossRefGoogle Scholar
Crowell, B. W., Bock, Y.and Squibb, M. B. (2009). Demonstration of earthquake early warning using total displacement waveforms from real-time GPS networks. Seismological Research Letters, 80(5), 772–782.CrossRefGoogle Scholar
Dai, L., Han, S., Wang, J.and Rizos, C. (2003). Comparison of interpolation algorithms in network-based GPS techniques. Navigation, 50(4), 277–293.CrossRefGoogle Scholar
Fotopoulos, G. (2000). Parameterization of Carrier Phase Corrections Based on a Regional Network of Reference Stations. Proceedings of the 13th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 2000). Salt Lake City, UT, September 2000, pp. 1093–1102.Google Scholar
Gao, Y. and Li, Z. (1998). Ionosphere Effect and Modeling for Regional Area Differential GPS Network. Proceedings of the 11th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1998), Nashville, TN, September 1998, pp. 91–98.Google Scholar
Gao, X. W., Liu, J. N.and Ge, M. R. (2002). An ambiguity searching method for network RTK baselines between base stations at single epoch. Acta Geodaetica et Cartographica Sinica, 31(4), 305–309.Google Scholar
Gao, W., Gao, C.and Pan, S. (2017a). A method of GPS/BDS/GLONASS combined RTK positioning for middle-long baseline with partial ambiguity resolution. Survey Review, 49(354), 212–220.CrossRefGoogle Scholar
Gao, W., Gao, C., Pan, S., Yu, G.and Hu, H. (2017b). Method and assessment of BDS triple-frequency ambiguity resolution for long-baseline network RTK. Advances in Space Research, 60(12), 2520–2532.CrossRefGoogle Scholar
Han, S. and Rizos, C. (1996). GPS Network Design and Error Mitigation for Real-Time Continuous Array Monitoring Systems. Proceedings of the 9th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1996), Kansas City, MO, September 1996, pp. 1827–1836.Google Scholar
Herring, T. A., King, R. W.and McClusky, S. C. (2010). Introduction to Gamit/Globk. Cambridge, Massachusetts: Massachusetts Institute of Technology.Google Scholar
Jiang, W. P., Zou, X.and Tang, W. M. (2012). A new kind of real-time PPP method for GPS single-frequency receiver using CORS network. Chinese Journal of Geophysics, 55(3), 284–293.CrossRefGoogle 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.CrossRefGoogle Scholar
Li, B., Shen, Y., Feng, Y., Gao, W.and Yang, L. (2014a). GNSS ambiguity resolution with controllable failure rate for long baseline network RTK. Journal of Geodesy, 88(2), 99–112.CrossRefGoogle Scholar
Li, B., Verhagen, S.and Teunissen, P. J. (2014b). Robustness of GNSS integer ambiguity resolution in the presence of atmospheric biases. GPS Solutions, 18(2), 283–296.CrossRefGoogle Scholar
Li, X., Zhang, X.and Ge, M. (2011). Regional reference network augmented precise point positioning for instantaneous ambiguity resolution. Journal of Geodesy, 85(3), 151–158.CrossRefGoogle Scholar
Meng, X., Yang, L., Aponte, J., Hill, C., Moore, T.and Dodson, A. H. (2008). Development of Satellite Based Positioning and Navigation Facilities for Precise ITS Applications. 2008 11th International IEEE Conference on Intelligent Transportation Systems. IEEE, 962–967.CrossRefGoogle Scholar
Odijk, D., van der Marel, H. and Song, I. (2000). Precise GPS positioning by applying ionospheric corrections from an active control network. GPS Solutions, 3(3), 49–57.CrossRefGoogle Scholar
Pan, S. G., Meng, X., Wang, S. L., Nie, W. F.and Chen, W. R. (2015). Ambiguity resolution with double troposphere parameter restriction for long range reference stations in NRTK System. Survey Review, 47(345), 429–437.CrossRefGoogle Scholar
Rizos, C. and Han, S. (2003). Reference station network based RTK systems – concepts and progress. Wuhan University Journal of Natural Sciences, 8(2), 566–574.CrossRefGoogle Scholar
Saastamoinen, J. (1972). Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. In Henriksen, S. W., Mancini, A., Chovitz, B. H. (eds.). The Use of Artificial Satellites for Geodesy, Geophysical Monographs Series, 15, American Geophysical Union, 247–251.Google Scholar
Shang, R., Gao, C., Pan, S., Wang, D.and Qiao, L. (2017). A Multi-Redundancies Network RTK Atmospheric Errors Interpolation Method Based on Delaunay Triangulated Network. In China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume III. Springer: Singapore, 321–335.CrossRefGoogle Scholar
Tang, W., Meng, X., Shi, C.and Liu, J. (2013). Algorithms for sparse network-based RTK GPS positioning and performance assessment. The Journal of Navigation, 66(3), 335–348.CrossRefGoogle Scholar
Teunissen, P. J. (1998). Success probability of integer GPS ambiguity rounding and bootstrapping. Journal of Geodesy, 72(10), 606–612.CrossRefGoogle Scholar
Teunissen, P. J. G. (1995). The least-squares ambiguity decorrelation adjustment: a method for fast GPS integer ambiguity estimation. Journal of Geodesy, 70, 1–2.CrossRefGoogle Scholar
Teunissen, P. J. and Verhagen, S. (2009). The GNSS ambiguity ratio-test revisited: a better way of using it. Survey Review, 41(312), 138–151.CrossRefGoogle Scholar
Wang, D., Gao, C.and Pan, S. (2012) Analysis and Modelling of Tropospheric Delay of Network RTK. In China Satellite Navigation Conference (CSNC) 2012 Proceedings. Guangzhou, Guangdong, China, pp 74–78.Google Scholar
Wanninger, L. (1995). Improved Ambiguity Resolution by Regional Differential Modelling of the Ionosphere. Proceedings of the ION GPS 95, 55–62.Google Scholar
Wanninger, L. (1999). The Performance of Virtual Reference Stations in Active Geodetic GPS-Networks Under Solar Maximum Conditions. Proceedings of the 12th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1999), Nashville, TN, September 1999, pp. 1419–1428.Google Scholar
Wu, S. (2009). Performance of regional atmospheric error models for NRTK in GPSnet and the implementation of a NRTK system. Ph.D. thesis, School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology University, Melbourne, Australia.Google Scholar
Wu, B., Gao, C., Pan, S., Deng, J.and Gao, W. (2015). Regional Modelling of Atmosphere Delay in Network RTK Based on Multiple Reference Station and Precision Analysis. In China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume II. Springer, Berlin, Heidelberg, 439–448.CrossRefGoogle Scholar
Yin, H., Huang, D.and Xiong, Y. (2008). Regional Tropospheric Delay Modelling Based on GPS Reference Station Network. In VI Hotine-Marussi Symposium on Theoretical and Computational Geodesy. Springer, Berlin, Heidelberg, 185–188.CrossRefGoogle Scholar
Zou, X., Wang, Y., Deng, C., Tang, W., Li, Z., Cui, J., Wang, C.and Shi, C. (2018). Instantaneous BDS+ GPS undifferenced NRTK positioning with dynamic atmospheric constraints. GPS Solutions, 22(1), 17.CrossRefGoogle Scholar