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| Robust Covariance Intersection Fusion Steady-state Kalman Filter for Uncertain Systems |
| Wang Xue-mei①② Liu Wen-qiang① Deng Zi-li① |
①(Electronic Engineering College, Heilongjiang University, Harbin 150080, China)
②(Department of Computer Science and Technology, Heilongjiang Technology and Business College, Harbin 150025, China) |
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Abstract For the linear discrete time multisensor system with uncertain model parameters and noise variances, a Covariance Intersection (CI) fusion robust steady-state Kalman filter based on the minimax robust estimation principle is presented. Firstly, introducing the fictitious noise, the model parameter uncertainty can be compensated, so the multisensory system with both the model parameter and noise variance uncertainties is converted into that with only uncertain noise variances. Secondly, using the Lyapunov equation, the robustness of the local robust Kalman filter is proved, so the robustness of the CI fused Kalman filter is guaranteed and it is proved that the robust accuracy of the CI fuser is higher than that of each local filter. Finally, a simulation example shows that how to search the robust region of uncertain parameters and shows the good performance of the proposed robust Kalman filter.
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Received: 27 November 2014
Published: 08 June 2015
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Corresponding Authors:
Deng Zi-li
E-mail: dzl@hlju.edu.cn
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