Identification of a time-varying parameter of a noiseless sinusoidal signal

AA Bobtsov, NA Nikolaev, OV Oskina…�- Automation and Remote�…, 2022 - Springer
AA Bobtsov, NA Nikolaev, OV Oskina, SI Nizovtsev
Automation and Remote Control, 2022Springer
We consider a new algorithm for estimating the time-varying parameter of a noiseless
sinusoidal signal. It is assumed that the unknown parameters and of the sinusoidal signal
are functions of time that are solutions of linear time-invariant differential equations with
known coefficients but unknown initial conditions. The problem is solved using gradient
tuning algorithms based on a linear regression equation obtained by parametrizing the
original parameter-nonlinear sinusoidal signal. An example and results of computer�…
Abstract
We consider a new algorithm for estimating the time-varying parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \omega (t) $$\end{document} of a noiseless sinusoidal signal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \alpha (t)\sin (\omega (t)+\varphi ) $$\end{document}. It is assumed that the unknown parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \alpha (t) $$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \omega (t) $$\end{document} of the sinusoidal signal are functions of time that are solutions of linear time-invariant differential equations with known coefficients but unknown initial conditions. The problem is solved using gradient tuning algorithms based on a linear regression equation obtained by parametrizing the original parameter-nonlinear sinusoidal signal. An example and results of computer simulation illustrate the efficiency of the proposed algorithm and also explain the procedure for its synthesis.
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