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An Improved Kalman Filter for Fractional Order System with Measurement Lévy noise

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Proceedings of the 2015 Chinese Intelligent Systems Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE))

Abstract

In this paper, taking the measurement noise as the non-Gaussian Lévy noise, an improved Kalman filter for discrete linear stochastic fractional order system is proposed. By eliminating the maximum of the noise, the Lévy noise can be approximated by a series of Gaussian white noises. Then, based on the principle of least square, an improved Kalman filter is developed for discrete linear stochastic fractional order system with measurement Lévy noise. Finally, simulation results are provided to illustrate the effectiveness and usefulness of the proposed filter designing algorithm, where a better filtering performance could be found.

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Acknowledgements

The work was supported in part by the National Natural Science Foundation of China under Grant 61104045, in part by the 111 Project (B14022), and in part by the Fundamental Research Funds for the Central Universities of China under Grant 2014B08014.

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Authors and Affiliations

  1. College of Energy and Electrical Engineering, Hohai University, Nanjing, 210098, China

    Yi Wang, Yonghui Sun, Zhenyang Gao, Xiaopeng Wu & Chao Yuan

Authors
  1. Yi Wang
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  2. Yonghui Sun
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  3. Zhenyang Gao
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  4. Xiaopeng Wu
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  5. Chao Yuan
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Corresponding author

Correspondence to Yonghui Sun .

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Editors and Affiliations

  1. Beihang University, Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. Tsinghua University, Beijing, China

    Hongbo Li

  4. University of Science & Technology Beijing, Beijing, China

    Weicun Zhang

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Wang, Y., Sun, Y., Gao, Z., Wu, X., Yuan, C. (2016). An Improved Kalman Filter for Fractional Order System with Measurement Lévy noise. In: Jia, Y., Du, J., Li, H., Zhang, W. (eds) Proceedings of the 2015 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48386-2_50

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  • DOI: https://doi.org/10.1007/978-3-662-48386-2_50

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48384-8

  • Online ISBN: 978-3-662-48386-2

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