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    Application of the Sign-Constrained Robust Least-Squares Method to Surveying Networks
    (ASCE-AMER SOC CIVIL ENGINEERS, 2013) Yetkin, Mevlut; Berber, Mustafa
    The least-squares (LS) method is highly susceptible to outlying observations. For this reason, various types of robust estimators have been developed; for example, M 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. DOI: 10.1061/(ASCE)SU.1943-5428.0000088. (C) 2013 American Society of Civil Engineers.
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    Comparison of accuracy of GPS techniques
    (ELSEVIER SCI LTD, 2012) Berber, Mustafa; Ustun, Aydin; Yetkin, Mevlut
    Accuracies of relative positioning techniques namely rapid static, pseudo-kinematic, kinematic, and real-time kinematic are investigated to determine their performances against static survey technique that yields the most precise results. Measurements are taken at seven National Geodetic Survey points along Road 714 in Florida using three sets of triple frequency Global Navigation Satellite System receivers. The data are processed using the software provided by the manufacturer. It turns out that pseudo-kinematic technique produces the closets results to static survey results. (C) 2012 Elsevier Ltd. All rights reserved.
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    Comparison of L-1 Norm and L-2 Norm Minimisation Methods in Trigonometric Levelling Networks
    (UNIV OSIJEK, TECH FAC, 2018) Inal, Cevat; Yetkin, Mevlut; Bulbul, Sercan; Bilgen, Burhaneddin
    The most widely-used parameter estimation method today is the L-2 norm minimisation method known as the Least Squares Method (LSM). The solution to the L-2 norm minimisation method is always unique and is easily computed. This method distributes errors and is sensitive to outlying measurements. Therefore, a robust technique known as the Least Absolute Values Method (LAVM) might be used for the detection of outliers and for the estimation of parameters. In this paper, the formulation of the L-1 norm minimisation method will be explained and the success of the method in the detection of gross errors will be investigated in a trigonometric levelling network.
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    ROBUSTNESS ANALYSIS OF GEODETIC NETWORKS IN THE CASE OF CORRELATED OBSERVATIONS
    (UNIV FEDERAL PARANA, CENTRO POLITECNICO, 2013) Yetkin, Mevlut; Berber, Mustafa; Inal, Cevat
    GPS (or GNSS) networks are invaluable tools for monitoring natural hazards such as earthquakes. However, blunders in GPS observations may be mistakenly interpreted as deformation. Therefore, robust networks are needed in deformation monitoring using GPS networks. Robustness analysis is a natural merger of reliability and strain and defined as the ability to resist deformations caused by the maximum undetectable errors as determined from internal reliability analysis. However, to obtain rigorously correct results; the correlations among the observations must be considered while computing maximum undetectable errors. Therefore, we propose to use the normalized reliability numbers instead of redundancy numbers (Baarda's approach) in robustness analysis of a GPS network. A simple mathematical relation showing the ratio between uncorrelated and correlated cases for maximum undetectable error is derived. The same ratio is also valid for the displacements. Numerical results show that if correlations among observations are ignored, dramatically different displacements can be obtained depending on the size of multiple correlation coefficients. Furthermore, when normalized reliability numbers are small, displacements get large, i.e., observations with low reliability numbers cause bigger displacements compared to observations with high reliability numbers.