Study on detection of gross error in geodetic network adjustment

• Autorzy: Kim J. H. , Kim C. J. , Li R. J.

Identyfikatory

DOI

10.24425/118696

• Języki publikacji: EN

Abstrakty

EN

Generally, gross errors exist in observations, and they affect the accuracy of results. We review methods to detect the gross errors by Robust estimation method based on L1-estimation theory and their validity in adjustment of geodetic networks with different condition. In order to detect the gross errors, we transform the weight of accidental model into equivalent one using not standardized residual but residual of observation, and apply this method to adjustment computation of triangulation network, traverse network, satellite geodetic network and so on. In triangulation network, we use a method of transforming into equivalent weight by residual and detect gross error in parameter adjustment without and with condition. The result from proposed method is compared with the one from using standardized residual as equivalent weight. In traverse network, we decide the weight by Helmert variance component estimation, and then detect gross errors and compare by the same way with triangulation network In satellite geodetic network in which observations are correlated, we detect gross errors transforming into equivalent correlation matrix by residual and variance inflation factor and the result is also compared with the result from using standardized residual. The results of detection are shown that it is more convenient and effective to detect gross errors by residual in geodetic network adjustment of various forms than detection by standardized residual.

Słowa kluczowe

EN

geodetic network adjustment; gross error; robust estimation; equivalent weight

PL

sieć geodezyjna; sieć triangulacyjna; geodezja satelitarna

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Geodesy and Cartography
• Rocznik: 2018
• Tom: Vol. 67, no. 1
• Strony: 57--69
• Opis fizyczny: Bibliogr. 16 poz., rys., tab., wykr.

Twórcy

autor

Kim J. H.

• Kim Chaek University of Technology Faculty of Resources Probing Engenering Kyogu 60, Pyongyang, D.P.R. Korea

autor

Kim C. J.

• Kim Chaek University of Technology Faculty of Resources Probing Engenering Kyogu 60, Pyongyang, D.P.R. Korea

autor

Li R. J.

• Kim Chaek University of Technology Faculty of Resources Probing Engenering Kyogu 60, Pyongyang, D.P.R. Korea

Bibliografia

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• [3] Ghilant, Ch.D. (2006). Adjustment Computations. John Wiley & Sons, Inc. 314–321.
• [4] Gui, Q.M., Gong, Y.S., Li, G.Z. and Li B.L. (2006). Bayesian Method for Detection of Gross Errors. Acta Geodetics et Cartographica Sinica, 35 (4), 303–307.
• [5] Gui, Q., Gong, Y., Li, G. and Li, B. (2007). A Bayesian Approach to the Detection of Gross Errors Based on Posterior Probability. J. Geod., 81, 651–659.
• [6] Guo, J.F., Ou, J.K. andWang, H.T. (2007). Quasi-accurate detection of outliers for correlated observation. Journal of Surveying Engineering, 3, 129–133.
• [7] Guo, J.K., and Wang, H.T. (2010). Robust estimation of correlation observations: Two local sensitivity–based down-weighting strategies. J. Geod., 4, 243–250.
• [8] Huber, P.J. and Ronhetti, E.M. (2009). Robust statistics. 2nd ed. John Wiley & Sons, Inc.
• [9] Kern, M., Preimesberger, T., Allesch, M., Pail, R., Bouman, J. and Koop, R. (2005). Outlier Detection Algorithms and Their Performance in GOCE Gravity. J. Geod., 78, 509–519.
• [10] Liu, C.J. and Ma, G.F. (2002). Blunder test and Robust solution of variance component estimation for Helmert type. Journal of Geomatics, 6.5–7.
• [11] Liu, J.N., Yao, Y. and Si, C. (2000). Theory Research on Robustified Least Squares estimator Based on Equivalent Variance-covariance. Science of Surveying and Mapping, 25 (3), 1–5. [in Chinese].
• [12] Ou, J.K., (1999). Quasi-accurate detection of gross errors (QUAD). Acta Geodaetica et Cartograghica Sinica, 1, 15–20.
• [13] Schaffrin, B. and Wang, Z. (1994). Multiplicative Outlier Search Using Hom BLUP and Equivalence Theorem. Manuscr. Geod., 20, 21–26.
• [14] Yang, Y.X., He, H.B. and Xu, G.C. (2001). A New Adaptively Robust Filtering for Kinematic Geodetic Positioning. J. Geod., 5 (2), 78–83.
• [15] Yang, Y.X., Song, L.J. and Xu, T.H. (2002). Robust Estimator for Correlated Observations Based on Bifactor Equivalent Weights. J. Geod., 76, 485–494.
• [16] Zumberge, J., Heflin, M. and Jefferson, D. (1997). Precise Point Positioning for the Efficient and Robust Analysis of GPS Data from Large Networks. Geophys. Res., 102, 5005–5017.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)