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Joint spatial registration and multi-target tracking using an extended PM-CPHD filter

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Abstract

An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.

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Authors and Affiliations

  1. SKLMSE Lab, MOE KLINNS Lab, School of Electronics and Information Engineering, **’an Jiaotong University, **’an, 710049, China

    Feng Lian, ChongZhao Han, **g Liu & **angHui Yuan

  2. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    WeiFeng Liu

Authors
  1. Feng Lian
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  2. ChongZhao Han
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  3. WeiFeng Liu
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  4. **g Liu
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  5. **angHui Yuan
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Correspondence to Feng Lian.

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Lian, F., Han, C., Liu, W. et al. Joint spatial registration and multi-target tracking using an extended PM-CPHD filter. Sci. China Inf. Sci. 55, 501–511 (2012). https://doi.org/10.1007/s11432-011-4531-1

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  • DOI: https://doi.org/10.1007/s11432-011-4531-1

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