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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2018-18-6-976-981
SINUSOIDAL SIGNAL PARAMETERS IDENTIFICATION WITH UNKNOWN VARIABLE AMPLITUDE
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Article in Russian
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Abstract
For citation:
Le Van Tuan, Bobtsov A. A. Sinusoidal signal parameters identification with unknown variable amplitude. Scientific and Technical Journal of Information Technologies, Mechanics and Optics , 2018, vol. 18, no. 6, pp. 976–981 (in Russian). doi: 10.17586/2226-1494-2018-18-6-976-981
Abstract
The paper considers the problem of the frequency identification for a biased sinusoidal signal in the absence of measurement noise. It is assumed that the displacement and amplitude of the sinusoidal signal are unknown functions of time. It is accepted that the frequency of the sinusoidal signal is an unknown number, and the displacement and amplitude of the sinusoidal signal can be represented as piecewise linear in the time interval. To estimate the frequency of the sinusoidal signal, an original parametrization procedure was proposed, reducing the original nonlinear equation to the form of a standard linear regression model. After a number of special transformations, the simplest equation was obtained, containing one unknown parameter (the square of the sinusoidal signal frequency) multiplied by the known time function. To search for this parameter, we used the standard integrated algorithm of identification, which makes it possible to guarantee the robustness of estimates to external disturbances, and also to improve the quality of transients due to the tuning coefficient. The proposed frequency identification algorithm has technical attractiveness and can be used in problems of compensation or suppression of disturbances and/or measurement errors described by harmonic or polyharmonic signals, including for compensation of vertical inertial accelerations in estimating gravity anomalies at a mobile object. To illustrate the efficiency of the proposed identification algorithm, the paper presents the results of computer modeling demonstrating the achievement of the target goals.
Keywords: identification, linear regression model, non-stationary parameters, sinusoidal signals, piecewise linear time functions
Acknowledgements. This work is supported by the Russian Science Foundation, project No. 18-19-00627.
References
Acknowledgements. This work is supported by the Russian Science Foundation, project No. 18-19-00627.
References
1. Aranovskiy S., Bobtsov A., Kremlev A., Nikolaev N., Slita O. Identification of frequency of biased harmonic signal. European Journal of Control, 2010, vol. 16, no. 2, pp. 129–139. doi: 10.3166/ejc.16.129-139
2. Hou M. Parameter identification of sinusoids. IEEE Transactions on Automatic Control, 2012, vol. 57, no. 2, pp. 467–472. doi: 10.1109/TAC.2011.2164736
3. Marino R., Tomei P. Frequency estimation of periodic signals. Proc. European Control Conference. Strasbourg, France, 2014, pp. 7–12. doi: 10.1109/ecc.2014.
4. Pyrkin A.A., Bobtsov A.A., Vedyakov A.A., Kolyubin S.A. Estimation of polyharmonic signal parameters. Automation and Remote Control, 2015, vol. 76, no. 8, pp. 1400–1416. doi: 10.1134/S0005117915080068
5. Bobtsov A.A., Efimov D., Pyrkin A.A., Zolghadri A. Switched algorithm for frequency estimation with noise rejection. IEEE Transactions on Automatic Control, 2012, vol. 57, no. 9, pp. 2400–2404. doi: 10.1109/TAC.2012.2186685
6. Aranovskiy S.V., Bobtsov A.A., Pyrkin A.A., Gritcenko P.A. Adaptive filters cascade applied to a frequency identification improvement problem. International Journal of Adaptive Controland Signal Processing, 2016, vol. 30, no. 5, pp. 677–689. doi: 10.1002/acs.2602
7. Aranovskiy S., Bobtsov A., Ortega R., Pyrkin A. Improved transients in multiple frequencies estimation via dynamic regressor extension and mixing. IFAC-PapersOnLine, 2016, vol. 49, no. 13, pp. 99–104. doi: 10.1016/j.ifacol.2016.07.934
8. Aranovskiy S., Bobtsov A., Ortega R., Pyrkin A. Performance enhancement of parameter estimators via dynamic regressor
extension and mixing. IEEE Transactions on Automatic Control, 2016, vol. 62, no. 7, pp. 3546–3550. doi: 10.1109/tac.2016.2614889
extension and mixing. IEEE Transactions on Automatic Control, 2016, vol. 62, no. 7, pp. 3546–3550. doi: 10.1109/tac.2016.2614889
9. Vedyakov A.A., Vediakova A.O., Bobtsov A.A., Pyrkin A.A., Aranovskiy S.V. A globally convergent frequency estimator of a sinusoidal signal with a time-varying amplitude. European JournalofControl,2017,vol. 38,pp. 32–38. doi: 10.1016/j.ejcon.2017.08.001
10. Le Van Tuan, Bobtsov A.A., Pyrkin A.A. New algorithm of variable parameters identification for linear regression model. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 5, pp. 952–955 (in Russian). doi: 10.17586/2226-1494-2017-17-5-952-955
11. Stepanov O.A., Blazhnov B.A., Koshaev D.A. Study of the effectiveness of using satellite measurements in determining the gravity acceleration on an aircraft. Giroskopiya i Navigatsiya, 2002, no. 3, pp. 33–47. (in Russian)
12. Stepanov O.A., Koshaev D.A. Analysis of filtering and smoothing techniques as applied to aerogravimetry. Gyroscopy and Navigation, 2010, vol. 1, no. 1, pp. 19–25. doi: 10.1134/s2075108710010049
13. Peshekhonov V.G., Stepanov O.A. et al. Modern methods and means of measuring the parameters of the Earth gravitational field. St. Petersburg, Elektropribor Publ., 2017, 390 p. (in Russian)
14. Ljung L. System Identification: Theory for the User. 2nd ed. New Jersey, Prentice-Hall, 1999, 409 p.
15. Miroshnik I.V., Nikiforov V.O., Fradkov A.L. Nonlinear and Adaptive Control of Complex Dynamic Systems. St. Petersburg, Nauka Publ., 2000, 549 p. (in Russian)
16. Andrievsky B.R., Fradkov A.L. Selected Chapters of Control Theory with Examples in MATLAB. St. Petersburg, Nauka Publ., 1999, 475 p. (in Russian)
Sastry S., Bodson M. Adaptive Control: Stability, Convergence and Robustness. Dover, 2011, 400 p.