Volume 30 Issue 3
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SUN Xiaohui, WEN Chenglin, WEN Tao. High-Order Extended Kalman Filter Design for a Class of Complex Dynamic Systems with Polynomial Nonlinearities[J]. Chinese Journal of Electronics, 2021, 30(3): 508-515. DOI: 10.1049/cje.2021.04.004
Citation: SUN Xiaohui, WEN Chenglin, WEN Tao. High-Order Extended Kalman Filter Design for a Class of Complex Dynamic Systems with Polynomial Nonlinearities[J]. Chinese Journal of Electronics, 2021, 30(3): 508-515. DOI: 10.1049/cje.2021.04.004
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High-Order Extended Kalman Filter Design for a Class of Complex Dynamic Systems with Polynomial Nonlinearities

  • 1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;

  • 2. School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China;

  • 3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds: 

This work is supported by the National Natural Science Foundation of China (No.61751304, No.61806064, No.61933013, No.U1664264) and Science and Technology Project of China Electric Power Research Institute (No.SGHB0000KXJS1800375).

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  • Received Date: June 28, 2020
    Graphical Abstract
    • Abstract
  • A novel High-order extended Kalman Filter (HEKF) is designed for a class of complex dynamic systems with polynomial nonlinearities. The state and measurement models are represented by multi-dimensional high-order polynomials, respectively. All high-order polynomials in the state model are defined as implicit variables. By combining original variables with implicit variables, the state model is equivalently formulated to be a pseudo-linear form. By modeling dynamic relationship between implicit variables and combining original variables with all implicit variables, a new linear augmented state model is established correspondingly. The measurement model can be equivalently formulated as a linear form. On the basis of the new linear state and linear measurement models, the HEKF is designed and derived in detail. Simulation results demonstrate the effectiveness of the proposed estimator.
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    • References (21)
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