Journal of Geodesy

, Volume 70, Issue 8, pp 489–498 | Cite as

Multiple outlier detection by evaluating redundancy contributions of observations

  • X. Ding
  • R. Coleman


When applying single outlier detection techniques, such as the Tau (τ) test, to examine the residuals of observations for outliers, the number of detected observations in any iteration of adjustment is most often more numerous than the actual number of true outliers. A new technique is proposed which estimates the number of outliers in a network by evaluating the redundancy contributions of the detected observations. In this way, a number of potential outliers can be identified and eliminated in each iteration of an adjustment. This leads to higher efficiency in data snooping of geodetic networks. The technique is illustrated with some numerical examples.


Actual Number Detection Technique Outlier Detection Geodetic Network Potential Outlier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 1996

Authors and Affiliations

  • X. Ding
    • 1
  • R. Coleman
    • 2
  1. 1.School of Surveying and Land InformationCurtin University of TechnologyPerthAustralia
  2. 2.School of Surveying and Spatial ScienceUniversity of TasmaniaHobartAustralia

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