Advertisement

GPS Solutions

, 23:26 | Cite as

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

  • Dezhong Chen
  • Shirong YeEmail author
  • Caijun Xu
  • Weiping Jiang
  • Peng Jiang
  • Hua Chen
Original Article
  • 132 Downloads

Abstract

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 5 min 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 97 km 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 111.5 s, 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.

Keywords

Tropospheric errors Undifferenced Ambiguity resolution CORS 

Notes

Acknowledgements

We are grateful to the anonymous reviewers and editors for their helpful suggestions and constructive comments, which have helped us significantly improve our paper. This work is partially supported by the National Science Fund for Distinguished Young Scholars (no. 41525014), National Natural Science Foundation of China (nos. 41704032, 41074008, 41431069, 41604028), Special Innovative Major Project of Hubei Province (no. 2018AAA066), Research Fund for the Doctoral Program of Higher Education of China (no. 20120141110025), Postdoctoral Science Foundation of China (no. 2018M642911), Anhui Natural Science Foundation (no. 1708085QD83), and Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (17-02-11).

References

  1. Alber C, Ware R, Rocken C, Braun J (2000) Obtaining single path phase delays from GPS double differences. Geophys Res Lett 27:2661–2664.  https://doi.org/10.1029/2000GL011525 CrossRefGoogle Scholar
  2. Black H (1978) An easily implemented algorithm for the tropospheric range correction. J Geophys Res 83(B4):1825–1828CrossRefGoogle Scholar
  3. Böhm J, Werl B, Schuh H (2006) Troposphere mapping functions for GPS and VLBI from ECMWF operational analysis data. J Geophys Res 111:B02406Google Scholar
  4. Chen X (1994) Analysis of continuous GPS data from the western Canada deformation array. In: Proc. ION GPS 1994, Institute of Navigation, Salt Lake City, September 20–23, pp 1339–1348Google Scholar
  5. Chen X, Han S, Rizos C, Goh P (2000) Improving real time positioning efficiency using the Singapore integrated multiple reference station network (SIMRSN). In: Proc. ION GPS 2000, Institute of Navigation, Salt Lake City, September 19–22, pp 9–18Google Scholar
  6. Dach R, Lutz S, Walser P, Fridez P (2015) Bernese GNSS software version 5.2. Astronomical Institute, University of BernGoogle Scholar
  7. Dai L, Wang J, Rizos C, Han S (2003) Predicting atmospheric biases for real-time ambiguity resolution in GPS/GLONASS reference station networks. J Geod 76(11):617–628CrossRefGoogle Scholar
  8. Feng Y (2008) GNSS three carrier ambiguity resolution using ionosphere-reduced virtual signals. J Geod 82(12):847–862CrossRefGoogle Scholar
  9. Grejner-Brzezinska D, Kashani I, Smith D, Spencer P, Robertson D (2004) An analysis of the effects of different network-based ionosphere estimation models on rover positioning accuracy. J GPS 3(1):115–131CrossRefGoogle Scholar
  10. Hatch R (1982) The synergism of GPS code and carrier measurements. In: Proc. the third international symposium on satellite doppler positioning at Physical Sciences Laboratory of New Mexico State University, Las Cruces, Feb 8–12, pp 1213–1231Google Scholar
  11. Hopfield H (1971) Tropospheric effect on electromagnetically measured range: prediction from surface weather data. Radio Sci 6:357–367CrossRefGoogle Scholar
  12. King R, Bock Y (2000) Documentation of the GAMIT GPS analysis software v. 9.9. Massachusetts Institute of Technology and Scripps Institution of OceanographyGoogle Scholar
  13. Li B, Teunissen PJG (2014) GNSS antenna array-aided CORS ambiguity resolution. J Geod 88(4):363–376CrossRefGoogle Scholar
  14. Li B, Feng Y, Shen Y (2010) Three carrier ambiguity resolution: distance-independent performance demonstrated using semi-generated triple frequency GPS signals. GPS Solut 14(2):177–184CrossRefGoogle Scholar
  15. Li B, Shen Y, Feng Y, Gao W, Yang L (2014) GNSS ambiguity resolution with controllable failure rate for long baseline network RTK. J Geod 88(2):99–112CrossRefGoogle Scholar
  16. Liu J, Ge M (2003) PANDA software and its preliminary result of positioning and orbit determination. Wuhan Univ J Nat Sci 8(2):603–609CrossRefGoogle Scholar
  17. Melbourne WG (1985) The case for ranging in GPS based geodetic systems. In: Proc. 1st international symposium on precise positioning with the global positioning system, Rockville, April 15–19, pp 373–386Google Scholar
  18. Montenbruck O, Steigenberger P, Hauschild A (2015) Broadcast versus precise ephemerides: a multi-GNSS perspective. GPS Solut 19(2):321–333CrossRefGoogle Scholar
  19. Odijk D (2000) Weighting ionospheric corrections to improve fast GPS positioning over medium distances. In: Proc. ION GPS 2000, Institute of Navigation, Salt Lake City, September 19–22, pp 1113–1123Google Scholar
  20. Parkins A (2011) Increasing GNSS RTK availability with a new single-epoch batch partial ambiguity resolution algorithm. GPS Solut 15(4):391–402CrossRefGoogle Scholar
  21. Saastamoinen J (1973) Contribution to the theory of atmospheric refraction. Bull Géod 107(1):13–34CrossRefGoogle Scholar
  22. Sun H, Cannon M, Melgard T (1999) Real-time GPS reference network carrier phase ambiguity resolution. In: Proc. ION/NTM 1999, San Diego, Jan 25–27, pp 193–199Google Scholar
  23. Verhagen S, Teunissen PJG (2006) New global navigation satellite system ambiguity resolution method compared to existing approaches. J Guid Control Dyn 29(4):981–991CrossRefGoogle Scholar
  24. Verhagen S, Teunissen PJG (2013) The ratio test for future GNSS ambiguity resolution. GPS Solut 17(4):535–548CrossRefGoogle Scholar
  25. Wang L, Verhagen S (2015) A new ambiguity acceptance test threshold determination method with controllable failure rate. J Geod 89(4):361–375CrossRefGoogle Scholar
  26. Wanninger L, Beer S (2015) BeiDou satellite-induced code pseudorange variations: diagnosis and therapy. GPS Solut 19(4):639–648CrossRefGoogle Scholar
  27. Wübbena G (1985) Software developments for geodetic positioning with GPS using TI 4100 code and carrier measurements. In: Proc. 1st International symposium on precise positioning with the global positioning system, Rockville, April 15–19, pp 403–412Google Scholar
  28. Zhang M, Liu H, Bai Z, Qian C, Fan C, Zhou P (2017) Fast ambiguity resolution for long-range reference station networks with ionospheric model constraint method. GPS Solut 21(2):617–626CrossRefGoogle Scholar
  29. Zhong P, Ding X, Yuan L, Xu Y, Kwok K, Chen Y (2010) Sidereal filtering based on single differences for mitigating GPS multipath effects on short baselines. J Geod 84(2):145–158CrossRefGoogle Scholar
  30. Zhu C, Zhenghang L, Xiaochuan Q (2015) Higher-order ionospheric effects on the GPS double difference observation and baseline vectors. J Geod Geodyn 35(1):81–86 (in Chinese) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Dezhong Chen
    • 1
  • Shirong Ye
    • 1
    Email author
  • Caijun Xu
    • 2
  • Weiping Jiang
    • 1
  • Peng Jiang
    • 3
  • Hua Chen
    • 2
  1. 1.GNSS Research CenterWuhan UniversityWuhanChina
  2. 2.School of Geodesy and GeomaticsWuhan UniversityWuhanChina
  3. 3.School of Resources and Environmental EngineeringAnhui UniversityHefeiChina

Personalised recommendations