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Iterated Particle Filter for Bearing Only Target Tracking

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International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018 (ATCI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 842))

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Abstract

Among many of the existing nonlinear filtering methods, particle filter has drawn special attention due to its ability to deal with nonlinear/non-Gaussian state estimation problems. But it suffers from degeneracy problem. In order the suppress this problem, in this paper, we introduced an iterated particle filter which is applied to the bearing only target tracking problem. The proposed method is on the base of the iterated Kalman filter which is used for building a proposal distribution for the particle filter. The iterated Kalman filter can improve the estimation accuracy by iteratively incorporating the current observations which makes it a better candidate for building proposal distribution and easy to be integrated into the particle filtering framework. Simulation results demonstrate that the iterated particle filter shows superior performance to the other methods in bearing only target tracking.

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Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian Minzu University, Dalian, 116600, China

    Fasheng Wang & Junxing Zhang

  2. Department of Software Engineering, Dalian Neusoft University of Information, Dalian, 116600, China

    Xucheng Li

  3. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China

    Baowei Lin

Authors
  1. Fasheng Wang
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  2. Xucheng Li
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  3. Baowei Lin
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  4. Junxing Zhang
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Corresponding author

Correspondence to Fasheng Wang .

Editor information

Editors and Affiliations

  1. Faculty of Science, Engineering and Built Environment, Deakin University Geelong, Geelong, VIC, Australia

    Jemal Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  4. Tsinghua University, Beijing Shi, China

    Zheng Xu

  5. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

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Wang, F., Li, X., Lin, B., Zhang, J. (2019). Iterated Particle Filter for Bearing Only Target Tracking. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_100

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