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Density Difference Grid Design in a Point-Mass Filter

Author

Listed:
  • Jakub Matoušek

    (Department of Cybernetics, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic
    These authors contributed equally to this work.)

  • Jindřich Duník

    (Department of Cybernetics, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic
    These authors contributed equally to this work.)

  • Ondřej Straka

    (Department of Cybernetics, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic
    These authors contributed equally to this work.)

Abstract

The paper deals with the Bayesian state estimation of nonlinear stochastic dynamic systems. The stress is laid on the point-mass filter, solving the Bayesian recursive relations for the state estimate conditional density computation using the deterministic grid-based numerical integration method. In particular, the grid design is discussed and the novel density difference grid is proposed. The proposed grid design covers such regions of the state-space where the conditional density is significantly spatially varying, by the dense grid. In other regions, a sparse grid is used to keep the computational complexity low. The proposed grid design is thoroughly discussed, analyzed, and illustrated in a numerical study.

Suggested Citation

  • Jakub Matoušek & Jindřich Duník & Ondřej Straka, 2020. "Density Difference Grid Design in a Point-Mass Filter," Energies, MDPI, vol. 13(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4080-:d:395578
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    References listed on IDEAS

    as
    1. Zhihao Yu & Ruituo Huai & Linjing Xiao, 2015. "State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization," Energies, MDPI, vol. 8(8), pages 1-20, July.
    2. Mitja Antončič & Igor Papič & Boštjan Blažič, 2019. "Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm," Energies, MDPI, vol. 12(23), pages 1-18, November.
    3. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    4. Peng Guo & David Infield, 2012. "Wind Turbine Tower Vibration Modeling and Monitoring by the Nonlinear State Estimation Technique (NSET)," Energies, MDPI, vol. 5(12), pages 1-15, December.
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