Application of the Sign-Constrained Robust Least-Squares Method to Surveying Networks
Publication: Journal of Surveying Engineering
Volume 139, Issue 1
Abstract
The least-squares (LS) method is highly susceptible to outlying observations. For this reason, various types of robust estimators have been developed; for example, estimators. In this paper, it is proposed to use the sign-constrained robust LS (SRLS) method in surveying networks utilizing the shuffled frog-leaping algorithm (SFLA). The robustness of SRLS is directly implemented as constraints. Therefore, a penalty function approach is used to deal with the constraints. In addition, the performance of any stochastic optimization approach such as SFLA strongly depends on the search domain. Hence, a strategy to define the boundaries of the search domain has been developed for use in surveying networks. The results indicate that SRLS yields better results than the LS method even if there are more outliers among the observations.
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Acknowledgments
The first author is grateful for the support by the Scientific and Technological Research Council of Turkey for his research at Florida Atlantic University.
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Information & Authors
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Published In
Journal of Surveying Engineering
Volume 139 • Issue 1 • February 2013
Pages: 59 - 65
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Nov 11, 2011
Accepted: May 24, 2012
Published online: May 29, 2012
Published in print: Feb 1, 2013
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