Volume 1 Issue 3
Sep.  2012
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Multiple Maneuvering Targets Tracking Using MM-CBMeMBer Filter

  • Received Date: 2012-05-28
    Accepted Date: 2012-07-02
  • The existing multiple model hypothesis density filter can estimate the number and state of maneuvering targets at the same time. Yet its Sequential Monte Carlo (SMC) implementation involves clustering algorithm, which is unstable and time consuming, and may result in tracking target loss. To solve the problem, this paper proposes a Multiple Model (MM) Cardinality Balanced Multiple target Multi-Bernoulli (CBMeMBer) filter. When the clutter number of per-scan is less than 20 and detection probability is higher than 0.9, this lgorithm transmits the posterior density of maneuvering targets through a set of time-varying Bernoulli parameters, according to which, the targets state can be computed by simple operations, thus effectively avoids the clustering algorithm. Simulation results shows that compared with multiple model hypothesis density filter, the algorithm proposed decreased the OSPA distance which chooses to estimate tracking errors.
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通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Multiple Maneuvering Targets Tracking Using MM-CBMeMBer Filter

    Corresponding author:
  • 1. (University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract: The existing multiple model hypothesis density filter can estimate the number and state of maneuvering targets at the same time. Yet its Sequential Monte Carlo (SMC) implementation involves clustering algorithm, which is unstable and time consuming, and may result in tracking target loss. To solve the problem, this paper proposes a Multiple Model (MM) Cardinality Balanced Multiple target Multi-Bernoulli (CBMeMBer) filter. When the clutter number of per-scan is less than 20 and detection probability is higher than 0.9, this lgorithm transmits the posterior density of maneuvering targets through a set of time-varying Bernoulli parameters, according to which, the targets state can be computed by simple operations, thus effectively avoids the clustering algorithm. Simulation results shows that compared with multiple model hypothesis density filter, the algorithm proposed decreased the OSPA distance which chooses to estimate tracking errors.

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