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Determination of measurement uncertainty by a Monte Carlo method for an RF power sensor calibration system using a VNA

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This work proposes a systematic assessment of measuring type A uncertainty (caused by random errors) used in RF power sensor calibration. To reduce A type uncertainty, several successive measurements are repeated. The uncertainty arises from repeatability errors in connectors caused by changes in their electrical properties during repeated mating. The suitability of the METAS UncLib software was analysed and we concluded that software should be developed to take into account the shape of probability density function (PDF) using a Monte Carlo method (MCM), which was lacking in METAS UncLib. The self-developed software was then tested on an example taken from the literature and the superiority of the MCM over the analytical method (GUM) was confirmed. During the calibration of the RF sensor using a vector network analyzer (VNA), a series of repeated measurements were performed and, after applying our MCM software, it was found that the measurement uncertainties calculated by the MCM method were several times larger than those by the GUM. The reason for this was that the correlation between the measured input quantities was not taken into account. When this was done using a covariance matrix and assuming a normal PDF of the input quantities, the results obtained with the GUM and the MCM converged. Our main objective was to investigate the influence of the PDF shape of the input measurement samples on the measurement uncertainty. Taking more than a dozen measurements is too costly, on the other hand, the small sample size prevents a reliable determination of the PDF shape. Finally, to overcome this inconvenience, we have developed a special method that uses the histograms of standardized input data taken at all measurement frequencies under fixed conditions without disconnecting the connectors, to increasing the total number of results which were needed to create the PDF histograms of input quantities.
Rocznik
Strony
703--720
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr., wzory
Twórcy
  • National Institute of Telecommunications (NIT), Warsaw, Poland
  • National Institute of Telecommunications (NIT), Warsaw, Poland
  • National Institute of Telecommunications (NIT), Warsaw, Poland
Bibliografia
  • [1] Joint Committee for Guides in Metrology. (2008). Evaluation of measurement data - Guide to the expression of uncertainty in measurement (JCGM 100:2008). http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf
  • [2] EA Laboratory Committee. (2013). EA-4/02. Evaluation of the uncertainty of measurement in calibration.
  • [3] Joint Committee for Guides in Metrology. (2008). Evaluation of measurement data - Supplement 1 to the “Guide to the expression of uncertainty in measurement” - Propagation of distributions using a Monte Carlo method (JCGM 101:2008). https://www.bipm.org/documents/20126/2071204/JCGM_101_2008_E.pdf
  • [4] Coral, R., Flesch, C. A., Penz, C. A., Roisenberg, M., & Pacheco, A. L. (2016). A Monte Carlo-based method for assessing the measurement uncertainty in the training and use of artificial neural networks. Metrology and Measurement Systems, 23(2), 281-294. https://doi.org/10.1515/mms-2016-0015
  • [5] Arkani, M. (2021). Observation probability estimation of dead-time models using Monte Carlo simulations. Metrology and Measurement Systems, 28(2), 383-395. https://doi.org/10.24425/mms.2021.136614
  • [6] Jaworski, M., et al. (2021). Research and development on calibration of measuring instruments in the field of electrical quantities and optoelectronics. NIT Report, Warsaw.
  • [7] Jaworski, M. (2022, May 5). Estimating the uncertainty of measurement using the Monte Carlo method, NIT Seminar, Warsaw.
  • [8] JCGM 102 (2011). Evaluation of measurement data - Supplement 2 to the “Guide to the expression of uncertainty in measurement” - Extension to any number of output quantities.
  • [9] Wu, T. Y., & Chua, S. W. (2009, May 5-7). Evaluation of mismatch uncertainty in microwave power sensor calibration using Monte Carlo method. Proceedings of the IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2009), Singapore.
  • [10] Zeier, M., Hoffmann, J., & Wollensack, M. (2012). Metas. UncLib - A measurement uncertainty calculator for advanced problems. Metrologia, 49(6), 809-815. https://doi.org/10.1088/0026-1394/49/6/809
  • [11] Wollensack, M. (2017). Introduction to METAS UncLib. Federal Institute of Metrology METAS
  • [12] Hall, B. D. (2022). The GUM tree calculator: A python package for measurement modelling and data processing with automatic evaluation of uncertainty. Metrology, 2(1), 128-149. https://doi.org/10.3390/metrology2010009
  • [13] Wollensack, M. (2022). METAS UncLib MATLAB - User Reference V2.6.0.
  • [14] Wollensack, M. (2022). METAS UncLib Python - User Reference V2.6.0.
  • [15] Juroshek, J. R. (1997). A direct calibration method for measuring equivalent source mismatch. Microwave Journal, 40(10), 106-113.
  • [16] Dobbert, M., & Gorin, J. (2011, August). Revisiting mismatch uncertainty with the Rayleigh distribution. In NCSL International Workshop and Symposium (pp. 8-9). Agilent Company.
  • [17] Szatkowski, J. (2021). Developing RF Power Sensor Calibration Station in Direct Comparison Transfer System using Vector Network Analyzer. Journal of Telecommunications and Information Technology. (3), 18-22. https://doi.org/10.26636/jtit.2021.155021
  • [18] Correlation and Regression Analysis (GNU Octave (version 6.3.0)). (n.d.). https://docs.octave.org/v6.3.0/Correlation-and-Regression-Analysis.html
  • [19] Krishnamoorthy, K. (2016). Handbook of Statistical Distributions with Applications. CRC Press.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6d35145e-6a7f-4248-8223-d89cf502c6f7
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