Model and Data Driven Partial Ambiguity Resolution for Multi-Constellation GNSS

  • Yanqing HouEmail author
  • Sandra Verhagen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 304)


The goal of carrier-phase ambiguity resolution is to exploit that the carrier-phase observations start to act as very precise pseudoranges. With the development of modern GNSS (GPS, BDS, Galileo, Glonass), more than 30 satellites are visible, however, it might be impossible to reliably fix all the ambiguities due to the computation time. Additionally, due to high measurement noise or residual atmosphere delays in case of longer baselines, the observation model is not strong enough, which makes it impossible to fix all the ambiguities. Therefore Partial Ambiguity Resolution (PAR) becomes more and more essential for real-time precise positioning. In this contribution, a Model and Data driven PAR (MD-PAR) strategy is proposed, and implemented in two different ways. The performance of MD-PAR is assessed using a simulation study by the probability of correct subset fixing, the subset size, and the Root Mean Square (RMS) of the baseline solution. Furthermore, MD-PAR is compared with the classic strategy, which uses only model information. The analysis and simulation results both suggest that the new strategies have better performance than the classic strategy.


Partial ambiguity resolution Model and data driven Multi-GNSS Success rate Criterion Ratio test 



The authors would like to acknowledge China Scholarship Council (CSC) for supporting the first author’s Ph.D. studies at the Delft University of Technology, Netherlands, and thank for the comments from Prof. Peter Teunissen, and the discussion with Lei Wang and Dr. Zishen Li.


  1. 1.
    Mowlam A (2004) Baseline precision results using triple frequency partial ambiguity sets. In: Procedings of ION GNSS-2004, Long Beach CA, pp 2509–2518Google Scholar
  2. 2.
    Takasu T, Yasuda A (2010) Kalman-filter-based integer ambiguity resolution strategy for long-baseline RTK with ionosphere and troposphere estimation. In: Proceedings of ION GNSS 2010, Portland, Oregon, pp 161–171Google Scholar
  3. 3.
    Parkins A (2011) Increasing GNSS RTK availability with a new single-epoch batch partial ambiguity resolution algorithm. GPS Solutions 15(4):391–402CrossRefGoogle Scholar
  4. 4.
    Dai L, Eslinger D, Sharpe T (2007) Innovative algorithms to improve long range RTK reliability and availability. In: Proceedings of ION NTM 2007, San Diego CA, pp 860–872Google Scholar
  5. 5.
    Yanming F, Li B (2008) Three carrier ambiguity resolution: generalised problems, models, methods and performance analysis using semi-generated triple frequency GPS data. In: Proceedings of ION GNSS 2008, Savannah GA, pp 2831–2840Google Scholar
  6. 6.
    Teunissen PJG, Joosten P, Tiberius CCJM (1999) Geometry-free ambiguity success rates in case of partial fixing. In: Proceedings of ION national technical meeting 1999 and 19th biennal guidance test symposium, San Diego CA, pp 201–207Google Scholar
  7. 7.
    Khanafseh S, Pervan B (2010) New approach for calculating position domain integrity risk for cycle resolution in carrier phase navigation systems. Trans Aerosp Electron Syst 46(1):296–307CrossRefGoogle Scholar
  8. 8.
    Teunissen PJG (1993) Least-squares estimation of the integer GPS ambiguities. In: Invited lecture, section IV “Theory and Methodology”, at the general meeting of the international association of Geodesy, Beijing, China, pp 1–16Google Scholar
  9. 9.
    Teunissen PJG (1995) The least-squares ambiguity decorrelation adjustment: a method for fast GPS integer ambiguity estimation. J Geodesy 1–2:65–82CrossRefGoogle Scholar
  10. 10.
    Henkel P (2009) Geometry-free linear combinations for Galileo. Acta Astronaut 65(9–10):1487–1499CrossRefGoogle Scholar
  11. 11.
    Henkel P, Günther C (2010) Partial integer decorrelation: optimum trade-off between variance reduction and bias amplification. J Geodesy 84(1):51–63CrossRefGoogle Scholar
  12. 12.
    Dai L, Eslinger DJ, Sharpe RT, Hatch RR (2011) Partial search carrier-phase integer ambiguity resolution. U.S. Patent 7,961,143 B2Google Scholar
  13. 13.
    Vollath U, Doucet KD (2009) GNSS signal processing with partial fixing of ambiguities. U.S. patent 7,538,721 B2Google Scholar
  14. 14.
    Vollath U (2010) Generalized partial fixing. U.S. patent 2010/0253575 A1Google Scholar
  15. 15.
    Lawrence DG (2009) A new method for partial ambiguity resolution. In: Proceedings of ION ITM 2009, Anaheim CA, pp 652–663Google Scholar
  16. 16.
    Teunissen PJG, Verhagen S (2009) The GNSS ambiguity ratio-test revisited: a better way of using it. Survey Review 151(April):138–151CrossRefGoogle Scholar
  17. 17.
    Teunissen PJG (1995) The invertible GPS ambiguity transformations. Manuscripta Geodaetica 20:489–497Google Scholar
  18. 18.
    Teunissen PJG (1997) The geometry-free GPS ambiguity search space with a weighted ionosphere. J Geodesy 71(6):370–383CrossRefzbMATHGoogle Scholar
  19. 19.
    Odijk D (2000) Stochastic modelling of the ionosphere for fast GPS ambiguity resolution. Geodesy Beyond 2000:387–392Google Scholar
  20. 20.
    Jin, X.X., Jong, C.D.d.: Relationship between satellite elevation and precision of GPS code observations. Journal of Navigation 49(2) (1996) 253–265Google Scholar
  21. 21.
    Teunissen PJG (1997) GPS ambiguity resolution: impact of time correlation, cross-correlation and satellite elevation dependence. Stud Geophys Geod 41(2):181–195CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Geoscience and Remote SensingDelft University of TechnologyDelftNetherlands

Personalised recommendations