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Evaluation of robust techniques in suppressing the impact of outliers in a deformation monitoring network – A case study on the Tehran Milad tower network
University West, Department of Engineering Science, Division of Computer, Electrical and Surveying Engineering. Division of Geodesy, Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0003-0067-8631
Division of Geodesy, Royal Institute of Technology, Stockholm, Sweden.
Department of Geodesy and Geomatics, Zanjan University, Zanjan, Iran.
2007 (English)In: Acta Geodaetica et Geophysica Hungarica, ISSN 1217-8977, Vol. 42, no 4, 449-463 p.Article in journal (Refereed) Published
Abstract [en]

The problem of handling outliers in a deformation monitoring network is of special importance, because the existence of outliers may lead to false deformation parameters. One of the approaches to detect the outliers is to use robust estimators. In this case the network points are computed by such a robust method, implying that the adjustment result is resisting systematic observation errors, and, in particular, it is insensitive to gross errors and even blunders. Since there are different approaches to robust estimation, the resulting estimated networks may differ. In this article, different robust estimation methods, such as the M-estimation of Huber, the “Danish”, and the L 1-norm estimation methods, are reviewed and compared with the standard least squares method to view their potentials to detect outliers in the Tehran Milad tower deformation network. The numerical studies show that the L 1-norm is able to detect and down-weight the outliers best, so it is selected as the favourable approach, but there is a lack of uniqueness. For comparison, Baarda’s method “data snooping” can achieve similar results when the outlier magnitude of an outlier is large enough to be detected; but robust methods are faster than the sequential data snooping process.

Place, publisher, year, edition, pages
Springer Netherlands, 2007. Vol. 42, no 4, 449-463 p.
Keyword [en]
Geodetic network, least squares, Milad tower, outlier detection, robust estimation
National Category
Other Engineering and Technologies not elsewhere specified Geophysics
Research subject
ENGINEERING
Identifiers
URN: urn:nbn:se:hv:diva-7896DOI: 10.1556/AGeod.42.2007.4.6OAI: oai:DiVA.org:hv-7896DiVA: diva2:845866
Available from: 2015-08-13 Created: 2015-08-13 Last updated: 2015-08-14Bibliographically approved

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