Abstract
The problem of adaptive estimation for measurement noise covariance matrix in Kalman filter is analytically solved based on accurate observations obtained irregularly. The results of numerical modeling are provided. These results illustrate the key advantages of state vector stochastic estimation algorithm based on proposed approach in comparison to conventional one.
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Acknowledgement
This work was supported by RFBR (Grants No. 17-20-01040 ofi_m_RZD, No. 18-07-00126).
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Sokolov, S.V., Sukhanov, A.V., Chub, E.G., Manin, A.A. (2020). Analytical Decision of Adaptive Estimation Task for Measurement Noise Covariance Matrix Based on Irregular Certain Observations. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_60
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DOI: https://doi.org/10.1007/978-3-030-50097-9_60
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