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Volume 42 Issue 10
Oct.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(10): 2082-2090.
YUAN Changshun, WANG Jun, ZHANG Yaotian, et al. Multiple maneuvering targets tracking with unknown clutter density using PHD[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(10): 2082-2090. doi: 10.13700/j.bh.1001-5965.2015.0623(in Chinese)
Citation: YUAN Changshun, WANG Jun, ZHANG Yaotian, et al. Multiple maneuvering targets tracking with unknown clutter density using PHD[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(10): 2082-2090. doi: 10.13700/j.bh.1001-5965.2015.0623(in Chinese)
shu

Multiple maneuvering targets tracking with unknown clutter density using PHD

doi: 10.13700/j.bh.1001-5965.2015.0623

  • School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Funds:  Natural Science Foundation of China (61171122, 61201318, 61471019, 61501011)
  • Received Date: 23 Sep 2015
  • Publish Date: 20 Oct 2016
    Abstract
  • The jump Markov system (JMS) based on the random finite set (RFS) is an effective approach for multiple maneuvering targets tracking. However, these approaches assume that the clutter density is known and priori. This is unrealistic for real applications, as it is often previously unknown and its value may be time-varying as the environment changes. To solve this problem, this paper proposes a novel algorithm for multiple maneuvering targets tracking with the linear Gaussian models in the case of unknown clutter density. The proposed method models the clutters and actual targets based on the Gaussian mixture probability hypothesis density filter with unknown clutter rate (λ-GMPHD), which removes the need of the prior clutter density, describes the maneuvering process by the linear Gaussian JMS and derives a closed-form solution to the GMPHD recursion for multiple maneuvering targets tracking under unknown clutter density. The simulation results indicate that the proposed algorithm can accurately estimate the target number and corresponding multi-target states as well as the clutter density.

     

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