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Robust predictive augmented unscented Kalman filter

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Abstract

This paper presents a new Unscented Kalman Filtering (UKF) method by using robust model prediction. This method incorporates system driving noise in system state through augmentation of state space dimension to expand the input of system state information. The system model error is constructed by model prediction, and is then used to rectify the UKF process to obtain the estimate of the real system state. The proposed method endows the robustness to the traditional UKF, thus overcoming the limitation that the traditional UKF is sensitive to system model error. Experimental results show that the convergence rate and accuracy of the proposed filtering method is superior to the Extended Kalman Filtering and traditional UKF.

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Author information

Authors and Affiliations

  1. School of Automatics, Northwestern Polytechnical University, Xi’an, 710072, China

    Yan Zhao & She-sheng Gao

  2. School of Management, Northwestern Polytechnical University, Xi’an, 710072, China

    Jing Zhang

  3. Xi’an Modern Chemistry Research Institute, Xi’an, 710065, China

    Qiao-nan Sun

Authors
  1. Yan Zhao
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  2. She-sheng Gao
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  3. Jing Zhang
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  4. Qiao-nan Sun
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Corresponding author

Correspondence to Yan Zhao.

Additional information

Yan Zhao received his D.R. degree in Traffic Information Engineering and Control from Northwestern Polytechnical University. in 2014. His research interests include nonlinear control, adaptive control and navigation in School of Automatics, Northwestern Polytechnical University.

She-sheng 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.

Jing Zhang received her M.S. degree in Public Management from Chang’an University in 2010. Her research interests include nonlinear control and systems engineering in School of Management, Northwestern Polytechnical University.

Qiao-nan Sun received his M.S. degree in Applied Chemistry from Tian Jin University in 2006. His research interests include process control, pesticide formulation, and solid propellant in Xi’an Modern Chemistry Research Institute.

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Zhao, Y., Gao, Ss., Zhang, J. et al. Robust predictive augmented unscented Kalman filter. Int. J. Control Autom. Syst. 12, 996–1004 (2014). https://doi.org/10.1007/s12555-013-0048-2

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  • DOI: https://doi.org/10.1007/s12555-013-0048-2

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