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Vestnik YuUrGU. Ser. Mat. Model. Progr., 2020, Volume 13, Issue 2, Pages 121–129 (Mi vyuru548)  

Programming and Computer Software

Parametric identification based on the adaptive unscented Kalman filter

V. M. Chubich, O. S. Chernikova

Novosibirsk State Technical University, Novosibirsk, Russian Federation

Abstract: The detailed adaptive unscented Kalman filter algorithm is provided. Step-by-step schemes of filtering algorithms used for the software development are given. Nonlinear filtering algorithm efficiency is investigated with considering an example of a nonlinear continuous-discrete model. The statistic estimator based on the continuous-discrete adaptive unscented Kalman filter with noise is proposed for the nonlinear system parameters estimation. The solution to the problem of solar radiation parameters estimation based on the maximum likelihood method and the adaptive unscented Kalman filter is shown. The obtained results lead to significant improvement of satellite trajectory prediction quality.

Keywords: nonlinear stochastic continuous-discrete system, adaptive unscented Kalman filter, parametric identification, ML method, spacecraft motion model, solar radiation model.

Funding Agency Grant Number
Ministry of Education and Science of the Russian Federation 2.7996.2017/8.9
The work was supported by the Ministry of Education and Science of the Russian Federation (project No 2.7996.2017/8.9).


DOI: https://doi.org/10.14529/mmp200210

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UDC: 51-74
MSC: 93E13
Received: 08.10.2019
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Citation: V. M. Chubich, O. S. Chernikova, “Parametric identification based on the adaptive unscented Kalman filter”, Vestnik YuUrGU. Ser. Mat. Model. Progr., 13:2 (2020), 121–129

Citation in format AMSBIB
\Bibitem{ChuChe20}
\by V.~M.~Chubich, O.~S.~Chernikova
\paper Parametric identification based on the adaptive unscented Kalman filter
\jour Vestnik YuUrGU. Ser. Mat. Model. Progr.
\yr 2020
\vol 13
\issue 2
\pages 121--129
\mathnet{http://mi.mathnet.ru/vyuru548}
\crossref{https://doi.org/10.14529/mmp200210}


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