| A novel extended Kalman filter for a class of nonlinear systems |
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| 引用本文： | DONG Zhe,YOU Zheng. A novel extended Kalman filter for a class of nonlinear systems[J]. 自然科学进展(英文版), 2006, 16(9): 912-918. DOI: 10.1080/10020070612330088 |
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| 作者姓名： | DONG Zhe YOU Zheng |
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| 作者单位： | Department of Precision Instrument and Mechanology, Tsinghua University, Beijing 100084, China |
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| 基金项目： | 教育部科学技术研究重点项目 |
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| 摘 要： | Estimation of the state variables of nonlinear systems is one of the fundamental and significant problems in control and signal processing. A new extended Kalman filtering approach for a class of nonlinear discrete-time systems in engineering is presented in this paper. In contrast to the celebrated extended Kalman filter (EKF), there is no linearization operation in the design procedure of the filter, and the parameters of the filter are obtained through minimizing a proper upper bound of the mean-square estimation error. Simulation results show that this filter can provide higher estimation precision than that provided by the EKF.
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| 关 键 词： | nonlinear systems state estimation Kalman filtering. |
A novel extended Kalman filter for a class of nonlinear systems |
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| DONG Zhe,YOU Zheng. A novel extended Kalman filter for a class of nonlinear systems[J]. Progress in Natural Science, 2006, 16(9): 912-918. DOI: 10.1080/10020070612330088 |
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| Authors: | DONG Zhe YOU Zheng |
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| Affiliation: | Department of Precision Instrument and Mechanology, Tsinghua University, Beijing 100084, China |
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| Abstract: | Estimation of the state variables of nonlinear systems is one of the fundamental and significant problems in control and signal processing. A new extended Kalman filtering approach for a class of nonlinear discrete-time systems in engineering is presented in this paper. In contrast to the celebrated extended Kalman filter (EKF), there is no linearization operation in the design procedure of the filter, and the parameters of the filter are obtained through minimizing a proper upper bound of the mean-square estimation error. Simulation results show that this filter can provide higher estimation precision than that provided by the EKF. |
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| Keywords: | nonlinear systems state estimation Kalman filtering |
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