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Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets

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  • Xianghui Yuan
  • Feng Lian
  • Chongzhao Han

Abstract

By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter.

Suggested Citation

  • Xianghui Yuan & Feng Lian & Chongzhao Han, 2013. "Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-16, December.
    • Handle: RePEc:hin:jnljam:727430
    DOI: 10.1155/2013/727430
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