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Maneuvering Target Tracking Based on Swarm Intelligent Unscented Particle Filtering

  • Conference paper
Artificial Intelligence and Computational Intelligence (AICI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7002))

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Abstract

To improve the performance of maneuvering target tracking, a based on Swarm intelligent unscented particle filtering was proposed. In the new filter, application of the un-scented Kalman filter is used to generate the proposal distribution. Moreover, by introducing the thought of artificial fish school algorithm into particle filtering, the particle distribution and filtering accuracy can be improved. In simulation experiment, “Coordinated Turns” model is taken as dynamic model of maneuvering target. The simulation results show that unscented particle filtering optimized by the artificial fish swarm algorithm (AFSA-UPF) has quite higher tracking precision than the PF and UPF by analyzing the tracking performance and the root-mean-square error.

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Author information

Authors and Affiliations

  1. Control Technology Research Institute, Taiyuan University of Technology, Taiyuan, 030024, China

    Yue-Long Wang & Fu-Chang Ma

  2. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

    Yue-Long Wang

Authors
  1. Yue-Long Wang
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  2. Fu-Chang Ma
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Editor information

Editors and Affiliations

  1. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, VIC, Australia

    Hepu Deng

  2. School of Electronics and Information, Tongji University, 201804, Shanghai, China

    Duoqian Miao

  3. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  4. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Wang, YL., Ma, FC. (2011). Maneuvering Target Tracking Based on Swarm Intelligent Unscented Particle Filtering. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23881-9_8

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  • DOI: https://doi.org/10.1007/978-3-642-23881-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23880-2

  • Online ISBN: 978-3-642-23881-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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