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Kalman Filter Algorithm Based on Sheep Herding Optimization

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Wireless Algorithms, Systems, and Applications (WASA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13472))

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Abstract

When dealing with the real track, the environment is often an unpredictable factor, so filtering is very important. We can use the filter to eliminate the influence of noise as much as possible. Kalman filter is one of them. In this work, we proposed a new Particle Swarm Optimization algorithm, called the Sheep Herding Optimization algorithm, which can obtain higher quality solutions with faster convergence speed and better stability. Besides, in order to improve the performance of Kalman filter, we apply the Sheep Herding Optimization algorithm to the filter. The improved Kalman filter can fuse and predict the track, and has higher computational performance and smaller error.

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Author information

Authors and Affiliations

  1. The 54th Research Institute of China Electronics Technology Group Corporation, Hebei, 050050, China

    Peng Wang

  2. Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control, Hebei, 050050, China

    Junyi Zhang

  3. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yuqi Zheng, Xiaohu Li & Yixin Li

Authors
  1. Peng Wang
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  2. Junyi Zhang
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  3. Yuqi Zheng
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  4. Xiaohu Li
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  5. Yixin Li
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Corresponding author

Correspondence to Yuqi Zheng .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Lei Wang

  2. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Michael Segal

  3. Chang Gung University, Taiwan, China

    Jenhui Chen

  4. Tianjin University, Tianjin, China

    Tie Qiu

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Wang, P., Zhang, J., Zheng, Y., Li, X., Li, Y. (2022). Kalman Filter Algorithm Based on Sheep Herding Optimization. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_27

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  • DOI: https://doi.org/10.1007/978-3-031-19214-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19213-5

  • Online ISBN: 978-3-031-19214-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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