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Multi-Target Tracking with Gaussian Mixture CPHD Filter and Gating Technique

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Non-Cooperative Target Tracking, Fusion and Control

Part of the book series: Information Fusion and Data Science ((IFDS))

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Abstract

Compared with the probability hypothesis density (PHD) filter, the cardinalized probability hypothesis density (CPHD) filter can give more accurate estimates of target number and the states of targets. The cost of increased accuracy is an increase in computational complexity. Fortunately, the computational cost of CPHD filter can be decreased by reducing the cardinality of measurement set. The gating techniques used in traditional tracking algorithms can be utilized to reduce the cardinality of measurement set. In this chapter, the elliptical gating technique is incorporated in the Gaussian mixture-CPHD filter to reduce the computational cost. Simulation results show that this method can improve the computational efficiency without losing too much estimation accuracy.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Zhongliang Jing & Han Pan

  2. University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yuankai Li

  3. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Peng Dong

Authors
  1. Zhongliang Jing
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  2. Han Pan
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  3. Yuankai Li
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  4. Peng Dong
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Jing, Z., Pan, H., Li, Y., Dong, P. (2018). Multi-Target Tracking with Gaussian Mixture CPHD Filter and Gating Technique. In: Non-Cooperative Target Tracking, Fusion and Control. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-90716-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-90716-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90715-4

  • Online ISBN: 978-3-319-90716-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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