Evaluation of the uncertainty type A of the random stationary signal component from its autocorrelated observations

• Autorzy: Warsza Z. L. , Dorozhovets M.
• Języki publikacji: EN

Abstrakty

EN

A proposal of evaluation of the uncertainty type A of the stationary random component of measured signal from its regularly sampled observations when they are auto-correlated is described. In the first step the regularly variable components of the signal are identified and removed from the raw sample data. Then upgreaded formulas for standard uncertainty type A of the sample and of the mean value are expressed with use the correction coefficients or the so-called "effective number" of observations. These quantities depend on number of observations and on the autocorrelation function of the sample cleaned from regular components. Two methods of finding and estimating the autocorrelation function for the sample data are also described. Some numerical examples are included.

Słowa kluczowe

EN

sampled signal; autocorrelated signal; stationary random component; effective number of observations

• Wydawca: Wydawnictwo PAK
• Czasopismo: Measurement Automation Monitoring
• Rocznik: 2015
• Tom: Vol. 61, No. 8
• Strony: 399--402
• Opis fizyczny: Bibliogr. 10 poz., rys., tab., wykr., wzory

Twórcy

autor

Warsza Z. L.

• Industrial Research Institute of Automation and Measurement (PIAP), 202 Jerozolimskie Ave., 02-486 Warszawa, Poland

autor

Dorozhovets M.

• Rzeszow University of Technology, 12 Powstańców Warszawy Ave., 35-959 Rzeszów, Poland
• National Technical University "Lviv Politechnic", 12 Stephan Bandera St., 79013 Lviv, Ukraine

Bibliografia

• [1] Guide to the Expression of Uncertainty in Measurement, revised and corrected. BIPM JCGM 100:2008.
• [2] Warsza Z. L, Dorozhovets M., Korczynski M. J.: Methods of upgrading the uncertainty of type A evaluation, Part 1 and Part 2, Proceedings of 15th IMEKO TC4 Symposium, 2007, Iasi Romania, 193-204.
• [3] Warsza Z. L., Dorozhovets M.: Uncertainty type A evaluation of autocorrelated measurement observations. Biuletyn WAT (Military Technical Academy) Warszawa, 2008, vol. LVII 2 143-152.
• [4] Zięba A.: Effective number of observations and unbiased estimators of variance for autocorrelated data – an overview. Metrology & Measurement Systems, 2010, no. 17, pp. 3-16.
• [5] Zięba A., Ramza P.: Standard deviation of the mean of autocorrelated observations estimated with the use of the autocorrelation function from the data. Metrology & Measurement Systems, 2011, no. 18, pp. 29-25.
• [6] Warsza Z. L.: Evaluation of the type A uncertainty in measure-ments with autocorrelated observations. CD Proceedings of jointed TC1+TC7+TC13 IMEKO Symposium. 2013, Genua - Journal of Physics: Conference Series 459, 012035 IOP Publishing.
• [7] Warsza Z. L., Korczynski M. J.: Improving of the type A uncertainty evaluation by refining the measurement data from a priori unknown systematic influences. Series: Advances in Intelligent Systems and Computing, no 267 (2014), Springer, pp. 727-732.
• [8] Dorozhovets M.: Using F - Test for the Indirect Detecting of the Autocorrelation of Random Observations. Proceedings of The 7th IEEE Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. 2013, Berlin.
• [9] Pollard J. H.: A Handbook of Numerical and Statistical Techniques. Cambridge University Press, 1977.
• [10] Warsza Z. L., Dorozhovets M.: Evaluation of the uncertainty type A of the random stationary signal component from its autocorrelated observations. Proceedings of 20th IMEKO TC4 International Symposium. Benevento, Italy, Sept.15-17, (2014), ISBN-14: 978-92-990073-2-7 p. 367 - 372 + ACTA IMEKO