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Bearing-Only Multiple Target Tracking with the Sequential PHD Filter for Multi-Sensor Fusion

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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

It is a widely studied problem to locate multiple emitters from passive angle measurements. The traditional multi-sensor passive multi-objective state estimation problem is a data association problem. In mathematics, the representation of data association problems leads to the generalization of the S-dimensional (S-D) assignment problem. Unfortunately, the complexity of solving an S-D assignment problem for S ≥ 3 is a nondeterministic polynomial (NP) hard problem. Multistage Lagrangian relaxation is a practical solution to solve the multidimensional assignment problem. However, its computational complexity rapidly increases with the sensor’s growth. In addition, satisfactory results cannot be achieved in dense clutter. In this chapter, we use the sequential Probability Hypothesis Density (PHD) filter for passive sensors in two different ways to solve the localization problem of multiple emitters. Simulation results verify the effectiveness of the algorithm. Compared with S-D assignment method, the proposed method can achieve better performance and with smaller computational complexity in environment with dense clutter.

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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). Bearing-Only Multiple Target Tracking with the Sequential PHD Filter for Multi-Sensor Fusion. 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_6

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  • DOI: https://doi.org/10.1007/978-3-319-90716-1_6

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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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