Volume 43 Issue 3
Mar.  2017
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MA Tianli, WANG Xinmin, CAO Yuyan, et al. A CDKF-CPHD multi-target tracking algorithm based on ML background parameter estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 516-523. doi: 10.13700/j.bh.1001-5965.2016.0189(in Chinese)
Citation: MA Tianli, WANG Xinmin, CAO Yuyan, et al. A CDKF-CPHD multi-target tracking algorithm based on ML background parameter estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 516-523. doi: 10.13700/j.bh.1001-5965.2016.0189(in Chinese)

A CDKF-CPHD multi-target tracking algorithm based on ML background parameter estimation

doi: 10.13700/j.bh.1001-5965.2016.0189
Funds:

Aeronautical Science Foundation of China 20152853029

  • Received Date: 10 Mar 2016
  • Accepted Date: 12 Jun 2016
  • Publish Date: 20 Mar 2017
  • Aimed at the problem of multiple maneuvering targets tracking in low signal-to-clutter ratio backgrounds, a central difference Kalman cardinalized probability hypothesis density filter based on maximum likelihood (ML) background parameter estimation (BE-CDKF-CPHD) is proposed. The ML method is used for estimating the parameters of heavy-tailed distribution, and calculating the detection probability and false alarm probability. The maximum-likelihood constant false alarm rate (ML-CFAR) is employed to process signals. In the CPHD filter, amplitude likelihood function is combined with the likelihood function of target position of the probability hypothesis density filter. The multiple maneuvering target tracking is fulfilled by estimating the mean and variance of posterior multi-target states with central difference Kalman filter. Simulation results show that the novel algorithm improves the estimate performance of target state and number.

     

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