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On the statistical analysis of the harmonic signal autocorrelation function

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents new tools for investigating the statistical properties of the harmonic signal autocorrelation function (ACF). These tools enable identification of the ACF estimator errors in measurements in which the triggering of the measurements is non-synchronized. This is important because in many measurement situations the initial phase of the measured signal is random. The developed tools enable testing the ACF estimator of a harmonic signal in the presence of Gaussian noise. These are the formulas on the basis of which the statistical properties of the estimator can be determined, including the bias, the variance and the mean squared error (MSE). For comparison, the article also presents the ACF statistical analysis tools used in the conditions of synchronized measurement triggering, known from the literature. Operation of the new tools is verified by simulation and experimental studies. The conducted research shows that differences between the MSE results obtained with the use of the developed formulas and those attained from simulations and experimental tests are not greater than 1 dB.
Rocznik
Strony
729--744
Opis fizyczny
Bibliogr. 45 poz., tab., wykr.
Twórcy
  • Institute of Metrology, Electronics and Computer Science, University of Zielona Góra, Szafrana 2, 65-516 Zielona Góra, Poland
  • Institute of Metrology, Electronics and Computer Science, University of Zielona Góra, Szafrana 2, 65-516 Zielona Góra, Poland
Bibliografia
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  • [24] Martinez, M.A. and Ashrafi, A. (2018). Real-valued single-tone frequency estimation using half-length autocorrelation, Digital Signal Processing 83: 98–106.
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  • [34] Sienkowski, S. and Krajewski, M. (2020). Single-tone frequency estimation based on reformed covariance for half-length autocorrelation, Metrology and Measurement Systems 27(3): 473–493.
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  • [36] Stodółka, J., Korzewa, L., Stodółka, W. and Gambal, J. (2017). The applicability of using parameters of the autocorrelation function in the assessment of human balance during quiet bipedal stance, Central European Journal of Sport Sciences and Medicine 17(1): 79–87.
  • [37] Tagade, P.M. and Choi, H.-L. (2017). A dynamic bi-orthogonal field equation approach to efficient Bayesian inversion, International Journal of Applied Mathematics and Computer Science 27(2): 229–243, DOI: 10.1515/amcs-2017-0016.
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  • [39] Tu, Y.-Q. and Shen, Y.-L. (2017). Phase correction autocorrelation-based frequency estimation method for sinusoidal signal, Signal Processing 130: 183–189.
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  • [42] Wang, K., Ding, J., Xia, Y., Liu, X., Hao, J. and Pei, W. (2018). Two high accuracy frequency estimation algorithms based on new autocorrelation-like function for noncircular/sinusoid signal, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 101(7): 1065–1073.
  • [43] Weber, R., Faye, C., Biraud, F. and Dansou, J. (1997). Spectral detector for interference time blanking using quantized correlator, Astronomy and Astrophysics Supplement Series 126(1): 161–167.
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  • [45] Zhang, M., Yang, Q., Chen, X. and Zhang, A. (2018). A generalized correlation theorem for LTI systems, Journal of Physics: Conference Series 1169(1): 012018.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7d43c6f8-e7b4-4544-b014-c8fad81c391d
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