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Adaptive and robust fractional gain based interpolatory cubature Kalman filter.

Mu, J., Tian, F and Cheng, J., 2023. Adaptive and robust fractional gain based interpolatory cubature Kalman filter. Measurement and Control. (In Press)

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mu-et-al-2023-adaptive-and-robust-fractional-gain-based-interpolatory-cubature-kalman-filter.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.1177/00202940231200954


In this study, we put forward the robust fractional gain based interpolatory cubature Kalman filter (FGBICKF) and the adaptive FGBICKF (AFGBICKF) for the development of the state estimators for stochastic nonlinear dynamics system. FGBICKF introduces a fractional gain to interpolatory cubature Kalman filter to increase the robustness of state estimation. AFGBICKF is developed to enhance the state estimation adaptive to stochastic nonlinear dynamics system with unknown process noise covariance through recursive estimation. The simulations on re-entry target tracking system have shown that the performance of FGBICKF is superior to that of cubature Kalman filter and interpolatory cubature Kalman filter, and standard deviation of FGBICKF is closer to posterior Cramér-Rao lower bound. Moreover, our simulations have also demonstrated that AFGBICKF remains stable even when the initial process noise covariance increase, proving its adaptiveness, robustness, and effectiveness on state estimation.

Item Type:Article
Uncontrolled Keywords:Fractional stochastic nonlinear dynamics system; interpolatory cubature rule; adaptive Kalman filter; state estimation
Group:Faculty of Science & Technology
ID Code:39157
Deposited By: Symplectic RT2
Deposited On:20 Nov 2023 13:22
Last Modified:20 Nov 2023 13:22


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