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.

Item Type:	Refereed Article
Research Division:	Engineering
Research Group:	Geomatic Engineering
Research Field:	Surveying (incl. Hydrographic Surveying)
Objective Division:	Expanding Knowledge
Objective Group:	Expanding Knowledge
Objective Field:	Expanding Knowledge in Philosophy and Religious Studies
UTAS Author:	Coleman, R (Professor Richard Coleman)
ID Code:	7438
Year Published:	1996
Web of Science® Times Cited:	24
Deposited By:	Surveying and Spatial Information Science
Deposited On:	1996-08-01
Last Modified:	2011-08-19
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