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Bearing-Only and Bearing-Doppler Target Tracking Based on EKF

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E-Learning and Games (Edutainment 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11462))

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Abstract

According to the characteristics of underwater target tracking, extended Kalman filter (EKF) algorithm was applied to underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. EKF is recursive Bayesian filter algorithm based on the linearization of the nonlinearities in the target state and the measurement equations. To ensure the observability in passive target tracking, we use single maneuvering observer. The simulation results show the suitability and effectiveness of the EKF algorithm to the single non-maneuverable target.

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References

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant No. 61703333) and the National Key Research and Development Program of China (grant No. 2017YFB1402103).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Xiaohua Li, Jiulong Zhang & Xiuxiu Li

  2. National University of Defense Technology, Changsha, 741200, China

    Chenxu Zhao

Authors
  1. Xiaohua Li
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  2. Chenxu Zhao
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  3. Jiulong Zhang
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  4. Xiuxiu Li
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Corresponding author

Correspondence to Xiaohua Li .

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Editors and Affiliations

  1. Liverpool John Moores University, Liverpool, UK

    Abdennour El Rhalibi

  2. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  3. Xi’an University of Technology, Xi’an, China

    Haiyan Jin

  4. Hangzhou Normal University, Hangzhou, China

    Dandan Ding

  5. Pontificia Universidad Javeriana, Cali, Colombia

    Andres A. Navarro-Newball

  6. Xi’an University of Technology, Xi’an, China

    Yinghui Wang

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Li, X., Zhao, C., Zhang, J., Li, X. (2019). Bearing-Only and Bearing-Doppler Target Tracking Based on EKF. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-23712-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23711-0

  • Online ISBN: 978-3-030-23712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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