Proceedings of the Faculty of Technical Sciences

Geodesy Engineering

Vol. 41 No. 07 (2026): Proceedings of the Faculty of Technical Sciences

Outlier Detection in Geodetic Measurements Using Different Methods

  • Čenge Mihalji
DOI:
https://doi.org/10.24867/35KG02Mihalji
Submitted
November 2, 2026
Published
2026-07-15

Abstract

This thesis examines the identification of outliers in geodetic networks, with special emphasis on the FSME method. After a brief theoretical overview of the Gauss–Markov model of indirect adjustment and the classical and robust approaches to outlier detection, their limitations are highlighted, particularly in cases involving multiple or weak outlier. The concept of the FSME method is presented, in which the adjustment model is extended with additional parameters to allow direct detection of outliers during the adjustment process. The practical part of the paper includes the application of the FSME method to a one-dimensional geodetic network and its comparison with classical and robust methods in order to perform a comparative analysis of the obtained results.

References

  1. [1] Батиловић, М. (2020). Робусна оцена деформација у слободним геодетским мрежама применом еволутивних оптимизационих алгоритама (Докторска дисертација). Нови Сад: Факултет техничких наука, Универзитет у Новом Саду.
  2. [2] Batilović, M., Sušić, Z., Marković, M., Vujinović, M., Nikolić, G., & Ninkov, T. (2018). The efficiency of different outlier detection approaches in geodetic networks: case study for Pobednik statue. Geodetski Vestnik, 62(02), 293–305.
  3. [3] Durdağ, U. M. (2024). Minimum-variance-based outlier detection method using forward-search model error in geodetic networks. Geoscientific Model Development, 17(6), 2187–2196.
  4. [4] Сушић, З., Нинков, Т., Батиловић, М., Булатовић, В., Сабадош, И., & Петковић, М. (2016). Примена геометријске анализе деформационих мерења у праћењу геодинамичких процеса. Нови Сад: Факултет техничких наука, Универзитет у Новом Саду.
  5. [5] Марковић, М. (2018). Метода одређивања деформација грађевинских структура применом фибер оптичких сензора (Докторска дисертација). Нови Сад: Факултет техничких наука, Универзитет у Новом Саду.
  6. [6] Wang, J., Chen, X., Shi, C., & Liu, J. (2023). Robust M-estimation-based ICKF for GNSS outlier mitigation in GNSS/SINS navigation applications. IEEE Transactions on Instrumentation and Measurement.
  7. [7] Erdogan, B., Hekimoğlu, S., Durdağ, U. M., & Öcalan, T. (2014). Empirical estimation of the power of test in outlier detection problem. Studia Geophysica et Geodaetica, 58(2), 262–279.
  8. [8] Hekimoğlu, S., & Koch, K. R. (1999). How can reliability of the test for outliers be measured? Allgemeine Vermessungsnachrichten, 106(11–12), 423–431.
  9. [9] Hekimoğlu, S., Erdogan, B., & Butterworth, S. (2010). Increasing the efficacy of the conventional deformation analysis methods: An alternative strategy. Journal of Surveying Engineering, 136(1), 3–12.
  10. [10] Hekimoğlu, S., Erdogan, B., Soycan, M., & Durdağ, U. M. (2011). A univariate approach for detecting outliers in geodetic networks. Journal of Surveying Engineering, 137(1), 1–10.