GPS Solutions

, Volume 18, Issue 2, pp 283–296 | Cite as

Robustness of GNSS integer ambiguity resolution in the presence of atmospheric biases

  • Bofeng Li
  • Sandra Verhagen
  • Peter J. G. Teunissen
Original Article


Both the underlying model strength and biases are two crucial factors for successful integer GNSS ambiguity resolution (AR) in real applications. In some cases, the biases can be adequately parameterized and an unbiased model can be formulated. However, such parameterization will, as trade-off, reduce the model strength as compared to the model in which the biases are ignored. The AR performance with the biased model may therefore be better than with the unbiased model, if the biases are sufficiently small. This would allow for faster AR using the biased model, after which the unbiased model can be used to estimate the remaining unknown parameters. We assess the bias-affected AR performance in the presence of tropospheric and ionospheric biases and compare it with the unbiased case. As a result, the maximum allowable biases are identified for different situations where CORS, static and kinematic baseline models are considered with different model settings. Depending on the size of the maximum allowable bias, a user may decide to use the biased model for AR or to use the unbiased model both for AR and estimating the other unknown parameters.


Ambiguity resolution Bias-affected success rate Tropospheric biases Ionospheric biases Ps-LAMBDA 



This work has been executed in the framework of the Positioning Program Project 1.01 “New carrier phase processing strategies for achieving precise and reliable multi-satellite, multi-frequency GNSS/RNSS positioning” in Australia of the Cooperative Research Centre for Spatial Information. PJG Teunissen is the recipient of an Australian Research Council (ARC) Federation Fellowship (project number FF0883188). This support is gratefully acknowledged.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bofeng Li
    • 1
    • 2
  • Sandra Verhagen
    • 3
  • Peter J. G. Teunissen
    • 1
    • 2
    • 3
  1. 1.College of Surveying and Geo-InformaticsTongji UniversityShanghaiPeople’s Republic of China
  2. 2.GNSS Research CentreCurtin UniversityPerthAustralia
  3. 3.Delft University of TechnologyDelftThe Netherlands

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