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

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Relaxation for online frequency estimator of bias-affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing. / Vedyakov, Alexey A.; Vediakova, Anastasiia O.; Bobtsov, Alexey A.; Pyrkin, Anton A.

In: International Journal of Adaptive Control and Signal Processing, Vol. 33, No. 12, 01.12.2019, p. 1857-1867.

Research output: Contribution to journal › Article › peer-review

Author

Vedyakov, Alexey A. ; Vediakova, Anastasiia O. ; Bobtsov, Alexey A. ; Pyrkin, Anton A. / Relaxation for online frequency estimator of bias-affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing. In: International Journal of Adaptive Control and Signal Processing. 2019 ; Vol. 33, No. 12. pp. 1857-1867.

BibTeX

@article{9788dd5557d445618a20d3f943c30291,

title = "Relaxation for online frequency estimator of bias-affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing",

abstract = "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.",

keywords = "continuous-time estimation, DREM, online frequency estimation, persistent excitation, relaxation",

author = "Vedyakov, {Alexey A.} and Vediakova, {Anastasiia O.} and Bobtsov, {Alexey A.} and Pyrkin, {Anton A.}",

year = "2019",

month = dec,

day = "1",

doi = "10.1002/acs.3034",

language = "English",

volume = "33",

pages = "1857--1867",

journal = "International Journal of Adaptive Control and Signal Processing",

issn = "0890-6327",

publisher = "Wiley-Blackwell",

number = "12",

}

RIS

TY - JOUR

T1 - Relaxation for online frequency estimator of bias-affected damped sinusoidal signals based on Dynamic Regressor Extension and Mixing

AU - Vedyakov, Alexey A.

AU - Vediakova, Anastasiia O.

AU - Bobtsov, Alexey A.

AU - Pyrkin, Anton A.

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

KW - continuous-time estimation

KW - DREM

KW - online frequency estimation

KW - persistent excitation

KW - relaxation

UR - http://www.scopus.com/inward/record.url?scp=85068157512&partnerID=8YFLogxK

U2 - 10.1002/acs.3034

DO - 10.1002/acs.3034

M3 - Article

AN - SCOPUS:85068157512

VL - 33

SP - 1857

EP - 1867

JO - International Journal of Adaptive Control and Signal Processing

JF - International Journal of Adaptive Control and Signal Processing

SN - 0890-6327

IS - 12

ER -

ID: 76547876