Skip to main content
SpringerLink
Log in

Methods of Data Processing in Measurements and Metrological Models

  • Published:
Measurement Techniques Aims and scope

The development of methods of data processing over the past 50 years is outlined. Distinctive features of data processing as a division of the theory of measurements are identified. The essential role and diversity of metrological models are emphasized. The significance of estimation of systematic measurement errors, including their randomization, is noted. The relationship between regression and confluent models is discussed within the framework of a discussion of joint measurements. Validation of data processing algorithms is considered as a practical approach to the comparison of the quality of different algorithms. The role of models in the determination and comparison of precision characteristics is demonstrated using the Allan variance as an example.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

References

  1. S. A. Aivazyan, Applied Statistics, Financy i Statistika, Moscow (1983).

    Google Scholar 

  2. F. Mosteller and J. Tukey, The Analysis of Data and Regression [Russian translation], Financy i Statistika, Moscow (1982).

    MATH  Google Scholar 

  3. V. A. Granovskii and T. N. Siraya, Methods of Processing Experimental Data in Measurements, Energoatomizdat, Leningrad (1990).

    Google Scholar 

  4. Yu. V. Tarbeev, V. S. Aleksandrov, L. I. Dovbeta, and T. N. Siraya, “Modern problems in theoretical metrology,” in: Itogi Nauki i Tekhniki, Vol. 8, Ser. Metrol. Izmer. Tekhn., VINITI (Moscow) (1991).

  5. V. V. Lyachnev (ed.), L. I. Dovbeta, and T. N. Siraya, Metrological Foundations of the Theory of Measurement Processes, Elmor, St. Petersburg (2011).

  6. M. F. Malikov, Foundations of Metrology. Part 1: Science of Measurements, Committee on Measures and Measuring Devices, Izd. Kom. po Delam Mer i Izmer. Priborov pri SM SSSR (1949).

  7. S. G. Rabinovich, Errors in Measurements, Energiya, Leningrad (1978).

    Google Scholar 

  8. S. G. Rabinovich, Measurement Errors and Uncertainties. Theory and Practice, Springer-Verlag, New York (2005).

    MATH  Google Scholar 

  9. M. A. Zemel’man, Metrological Foundations of Engineering Measurements, Izd. Standartov, Moscow (1991).

    Google Scholar 

  10. L. A. Semenov and T. N. Siraya, Methods of Constructing Calibration Characteristics of Measuring Instruments, Izd. Standartov, Moscow (1986).

    Google Scholar 

  11. M. G. Cox, “Systematic-error modeling, with application to complex permittivity measurement,” in: 16th IMEKO TC4 Symp., Florence (2008).

  12. Yu. V. Linnik, Method of Least Squares and the Foundations of the Theory of Processing Observations, Fizmatgiz, Moscow (1958).

    Google Scholar 

  13. Ye. Z. Demidenko, Linear and Nonlinear Regressions [Russian translation], Financy i Statistika, Moscow (1981).

    Google Scholar 

  14. W. Stahel, “Robust alternatives to least squares,” Adv. Math. Tools in Metrol. III, Ser. Adv. Math. for Appl. Sci., 45, 118–133 (1997).

    Google Scholar 

  15. Yu. V. Tarbeev, I. B. Chelpanov, and T. N. Siraya, “Certifi cation of data processing algorithms in measurements,” Izmer., Kontrol, Avtomatiz., No. 3, 3–13 (1991).

  16. A. G. Chunovkina and A. V. Chursin, “Certification of algorithms for determination of signal extreme values during measurements,” Adv. Math. Tools in Metrol. III, Ser. Adv. Math. for Appl. Sci., 45, 165–170 (1997).

    Google Scholar 

  17. T. Siraya, “Certification of algorithms for constructing calibration curves of measuring instruments,” Adv. Math. and Comp. Tools in Metrol. and Testing. X, Ser. Adv. Math. for Appl. Sci., 86 368–376 (2015).

    Google Scholar 

  18. D. W. Allan, N. Ashby, and C. C. Hodges, The Science of Timekeeping. Applic. Note 1289, Hewlett-Packard Company, Palo Alto (1997).

    Google Scholar 

  19. D. W. Allan, “Historicity, strengths, and weaknesses of Allan variances and their general applications,” in: Proc. 22nd S.-Petersburg Int. Conf. on Integrated Navigation Systems, Concern CSRI Elektropribor, St. Petersburg (2015).

  20. A. M. Yaglom, Correlation Theory of Stationary and Related Random Functions, Springer-Verlag, New York (1987).

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Research Center of the Russian Federation – Concern CSRI Elektropribor, St. Petersburg, Russia

    T. N. Siraya

Authors
  1. T. N. Siraya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. N. Siraya.

Additional information

Translated from Izmeritel’naya Tekhnika, No. 1, pp. 9–14, January, 2018.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Siraya, T.N. Methods of Data Processing in Measurements and Metrological Models. Meas Tech 61, 9–16 (2018). https://doi.org/10.1007/s11018-018-1380-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11018-018-1380-y

Keywords

Search

Navigation