Robust Testing Procedure for Detection of Multiple Blunders

Abstract

Blunder detection is a topic of great interest to surveyors and mappers because undetected blunders significantly distort the solution. Therefore, a robust testing procedure has been developed for the detection and identification of multiple blunders in survey data. In the method, the theories of robust estimation and statistical hypothesis testing are successfully integrated to provide a reliable and unified testing process. The method can be applied to the case of multiple blunders and can greatly improve the detection and isolation of blunders as compared to the statistical testing of estimated residuals from a conventional least‐squares process. The conventional least‐squares procedure tends to smooth the blunder into good observations, whereas the proposed method does not. Two numerical examples are given to test and illustrate the performance of the proposed method for cases of single and multiple blunders.