Frequency estimation of a sinusoidal signal with time-varying amplitude and phase

Vedyakov, Alexey A.; Vediakova, Anastasiia O.; Bobtsov, Alexey A.; Pyrkin, Anton A.; Kakanov, Mikhail A.

doi:10.1016/j.ifacol.2018.11.501

Cited by 9 publications

(12 citation statements)

References 15 publications

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“…at the predefined time t f t . An alternative method of building frequency estimates ω f t i (t) using ĉ f t i without the inverse trigonometric function arccos{•} is described in the paper [24].…”

Section: Finite-time Parameter Estimationmentioning

confidence: 99%

Frequency Estimation of Multi-Sinusoidal Signals in Finite-Time

Vediakova,

Vedyakov,

Pyrkin

et al. 2020

Preprint

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This paper considers the problem of frequency estimation for a multi-sinusoidal signal consisting of n sinuses in finite-time. The parameterization approach based on applying delay operators to a measurable signal is used. The result is the nth order linear regression model with n parameters, which depends on the signals frequencies. We propose to use Dynamic Regressor Extension and Mixing method to replace nth order regression model with n first-order regression models. Then the standard gradient descent method is used to estimate separately for each the regression model parameter. On the next step using algebraic equations finite-time frequency estimate is found. The described method does not require measuring or calculating derivatives of the input signal, and uses only the signal measurement. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

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Section: Finite-time Parameter Estimationmentioning

confidence: 99%

Frequency Estimation of Multi-Sinusoidal Signals in Finite-Time

Vediakova,

Vedyakov,

Pyrkin

et al. 2020

Preprint

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show abstract

“…If there is no noise, the slope A of the line is the ratio of standard frequency f s to measured frequency f x , according to (11), which can be written as (19), and can be deduced by any two data selected form line 'L' in Fig. 4.…”

Section: Modellingmentioning

confidence: 99%

“…4. So 18is the proposed linear model for frequency measurement A = f s f x (19) Actually, if the real data (i, Y′(i)) is brought directly into (18), there must be a residual value because of the noise in signal, which is shown as below:…”

Section: Modellingmentioning

confidence: 99%

“…Prony method descripted in [18] is used to estimate the parameters of the FID signal. An algorithm based on standard gradient approach is proposed in [19] to estimate the frequency of signal with time-varying amplitude. In the frequency domain method, DFT method [20] is the most direct method for low SNR sine signal frequency estimation, and a lot of improved DFT method [21][22][23][24] are developed to increase the precision.…”

Section: Introductionmentioning

confidence: 99%

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Frequency measurement approach based on linear model for increasing low SNR sinusoidal signal frequency measurement precision

Tan

Yue

Wang

2019

IET Science, Measurement & Technology

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“…Assuming a sinusoidal input signal W with unknown frequency and a measurable output Δt, the problem becomes the same as (3). For the classical two-phase model of an unsaturated nonsalient PMSM 13,14 in our other work, 15 it is shown that angular velocity estimation problem can be transformed to frequency estimation of a harmonic signal with time-varying and measurable amplitude and phase.…”

mentioning

confidence: 99%

Relaxation for online frequency estimator of bias‐affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing

Vedyakov

Vediakova

Bobtsov

et al. 2019

Adaptive Control & Signal

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This paper considers the problem of continuous-time online frequency estimation for a biased damped sinusoidal signal. The previous result for a sinusoidal signal with time-varying amplitude requires a persistency of excitation condition for regressor, which is not satisfied in the considered case. To relax this condition, we propose to use Dynamic Regressor Extension and Mixing method on the first step to replace nth-order regression with n first-order regression models. On the second step, two simple relaxation methods are proposed to establish necessary excitation for the first-order gradient-based estimator. The efficiency of the proposed approach is demonstrated through the set of numerical simulations for the exponentially damped sinusoidal signal.

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Frequency estimation of a sinusoidal signal with time-varying amplitude and phase

Cited by 9 publications

References 15 publications

Frequency Estimation of Multi-Sinusoidal Signals in Finite-Time

Frequency Estimation of Multi-Sinusoidal Signals in Finite-Time

Frequency measurement approach based on linear model for increasing low SNR sinusoidal signal frequency measurement precision

Relaxation for online frequency estimator of bias‐affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing

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