oa Investigating Systematic Errors in Geodetic Instruments: A Monte Carlo Simulation Approach

References

1. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML (2011) Supplement 2 to the Guide to the Expression of Uncertainty in Measurement-Extension to any number of output quantities. JCGM 102:2011
   [Google Scholar]
2. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, and OIML. Evaluation of measurement data – Supplement 1 to the “Guide to the expression of uncertainty in measurement” – Propagation of distributions using a Monte Carlo method. Joint Committee for Guides in Metrology, JCGM 101:2008. doi:10.59161/JCGM101‑2008.
   https://doi.org/10.59161/JCGM101-2008 [Google Scholar]
3. Bonimani, M. L. S., Rofatto, V. F., Matsuoka, M. T., & Klein, I. (2019). Aplicação de Números Aleatórios Artificiais e Método Monte Carlo na Análise de Confiabilidade de Redes Geodésicas [Application of Artificial Random Numbers and the Monte Carlo Method to Reliability Analysis of Geodetic Networks]. Revista Brasileira de Computação Aplicada, 11(2), 74–85. https://doi.org/10.5335/rbca.v11i2.8906
   [Google Scholar]
4. DSTU 8955:2019 Metrolohiia. Teodolity i takheometry. Metrolohichni ta tekhnichni vymohy [Metrology. Theodolites and total stations. Metrological and technical requirements]. – Kyiv. DP «UkrNDNTs». – 2020
   [Google Scholar]
5. Erenoğlu,R. Cüneyt (2018). A Novel Robust Scaling for EDM Calibration Baselines using Monte Carlo Study. Tehnicki vjesnik – Technical Gazette, 25(1). https://doi.org/10.17559/tv-20160407214150
   [Google Scholar]
6. Niemeier, W., & Tengen, D. (2017). Uncertainty assessment in geodetic network adjustment by combining GUM and Monte-Carlo-simulations. Journal of Applied Geodesy, 11(2). https://doi.org/10.1515/jag-2016-0017
   [Google Scholar]
7. Savchyn, I., Tretyak, K., Brusak, I., Lozynskyi, V., & Duma, M. (2023, October). Rapid Fixation and Digitization for Cultural Heritage Preservation in Conflict Zones. In International Conference of Young Professionals «GeoTerrace-2023» (Vol. 2023, No. 1, pp. 1–5). European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214-4609.2023510030
   [Google Scholar]
8. Shults, R. V., & Sossa, B. R. (2015). Systemne kalibruvannia nazemnykh lazernykh skaneriv: modeli ta metodyky [System calibration of terrestrial laser scanners: models and methodologies]. Visnyk heodezii ta kartohrafii, (2), 25–30.
   [Google Scholar]
9. Sossa, B. & Havryshchuk, V. (2025). Vykorystannia metodu Monte-Karlo pry doslidzhenni pokhybok heodezychnykh pryladiv [Application of the Monte Carlo method in the study of errors of geodetic instruments]. Technical sciences and technologies. Scientific journal. 2(40), 472–484https://doi.org/10.25140/2411-5363-2025-2(40)-472-484
   [Google Scholar]
10. Tomczyk, K. (2018). Influence of Monte Carlo generations applied for modelling of measuring instruments on maximum distance error. Transactions of the Institute of Measurement and Control, 41(1), 74–84. https://doi.org/10.1177/0142331217753062
    [Google Scholar]
11. Wang, L., & Luo, X. (2021). Adaptive Quasi-Monte Carlo method for nonlinear function error propagation and its application in geodetic measurement. Measurement, 186, 110122. https://doi.org/10.1016/j.measurement.2021.110122
    [Google Scholar]
12. Wyszkowska, P. (2017). Propagation of uncertainty by Monte Carlo simulations in case of basic geodetic computations. Geodesy and Cartography, 66(2), 333–346. https://doi.org/10.1515/geocart-2017-0022
    [Google Scholar]