Multiple outlier detection by evaluating redundancy contributions of observations |
| |
Authors: | X Ding R Coleman |
| |
Institution: | (1) School of Surveying and Land Information, Curtin University of Technology, GPO Box U 1987, 6001 Perth, WA, Australia;(2) School of Surveying and Spatial Science, University of Tasmania, GPO Box 252 C, 7001 Hobart, Tasmania, Australia |
| |
Abstract: | When applying single outlier detection techniques, such as the Tau () 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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|