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Journal of Projectiles, Rockets, Missiles and Guidance >

2015 , Vol. 35 >Issue 5: 17 - 21

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2015.05.005

MISSILES AND GUIDANCE TECHNOLOGY

Gaussian Mixture Particle Probability Hypothesis Density Filter Based on Unscented Transform in Multi-target Tracking

  • LIU Xin ,
  • FENG Xinxi ,
  • KONG Yunbo ,
  • WANG Jing
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  • 1 Information and Navigation College, Air Force Engineering University, Xi'an 710077, China
    2 No. 94969 Unit, Shanghai 200400, China

Received date: 2014-11-19

  Online published: 2025-05-28

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Abstract

Considering lower estimating accuracy and filtering divergence of traditional GMP-PHD algorithm in clutter environment, a modified GMP-PHD based on unscented transform was proposed. On the basis of the GMP-PHD prediction, the algorithm applies unscented transform to importance sampling, updates the sampling particles combined with observation values to get the importance density function, and then update the GMP-PHD function. The performance of the proposed algorithm was compared with traditional GMP-PHD algorithm. The simulation results show that the proposed algorithm can promote not only the accuracy but also the stability of the system.

Key words： multiple target tracking; probability hypothesis density; unscented transform; Gauss hybrid particles PHD

Cite this article

LIU Xin , FENG Xinxi , KONG Yunbo , WANG Jing . Gaussian Mixture Particle Probability Hypothesis Density Filter Based on Unscented Transform in Multi-target Tracking[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2015 , 35(5) : 17 -21 . DOI: 10.15892/j.cnki.djzdxb.2015.05.005

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