Multiple outlier detection by evaluating redundancy contributions of observations

Xiaoli Ding, R. Coleman

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)489-498
Number of pages10
JournalJournal of Geodesy
Volume70
Issue number8
DOIs
Publication statusPublished - 1 Jan 1996
Externally publishedYes

ASJC Scopus subject areas

  • Geophysics
  • Geochemistry and Petrology
  • Computers in Earth Sciences

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