Abstract
The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamic systems due to its simple calculation process and superior performance in highly nonlinear systems. However, its solution will be degraded or even divergent when the system model involves uncertainty. This paper presents an interacting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This method combines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbance of system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process model uncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principle of innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF and robust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilistic weighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparison analysis validate the efficacy of the proposed method.
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Recommended by Associate Editor Yingmin Jia under the direction of Editor Duk-Sun Shim. The work of this paper was supported by the National Natural Science Foundation of China (Project Number: 61174193) and the Specialized Research Fund for the Doctoral Program of Higher Education (Project Number: 20136102110036).
Bingbing Gao is a PhD student at the School of Automatics, Northwestern Polytechnical University, China. His research interests include information fusion, nonlinear filtering and integrated navigation.
Shesheng Gao is a Professor at the School of Automatics, Northwestern Polytechnical University, China. His research interests include control theory and engineering, navigation, guidance and control, and information fusion.
Yongmin Zhong is an Associate Professor in the School of Engineering at RMIT University. His research interests include computational engineering, virtual reality, haptics, soft tissue modelling and surgery simulation, aerospace navigation and control, intelligent systems and robotics.
Gaoge Hu received the D.S. degree in control theory and control engineering from Northwestern Polytechnical University in 2016. His research interests include information fusion, nonlinear filtering and integrated navigation.
Chengfan Gu is a lecturer in the School of Engineering at RMIT University, Australia. Prior to this, she was an ARC DECRA Fellow with UNSW Australia. Her research interests include bio/nano materials characterization and analysis, materials processing, computational modelling and optimization analysis.
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Gao, B., Gao, S., Zhong, Y. et al. Interacting multiple model estimation-based adaptive robust unscented Kalman filter. Int. J. Control Autom. Syst. 15, 2013–2025 (2017). https://doi.org/10.1007/s12555-016-0589-2
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DOI: https://doi.org/10.1007/s12555-016-0589-2