Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (5): 961-967.doi: 10.3969/j.issn.1001-506X.2018.05.01

    Next Articles

Box particle CPHD filter for group targets tracking based on evolution network model

CHENG Xuan, SONG Liping, JI Hongbing   

  1. School of Electronic Engineering, Xidian University, Xi’an 710071, China
  • Online:2018-04-28 Published:2018-04-24

PDF (PC)

Abstract:

This paper proposes a cardinalized probability hypothesis density (CPHD) group target tracking algorithm based on the evolving network model and interval analysis. Currently the traditional particle CPHD group target tracking algorithm causes a large number of particles and a large amount of computing. The proposed algorithm uses box particle filtering to realize CPHD filter, which decreases the number of particles and computation complexity. Through predicting and updating group target state based on CPHD filtering and modifying the target state in the group by using group information, the algorithm can realize tracking for group target centroid and obtain cardinality statistics for group targets. The simulation experiments show that, the proposed algorithm greatly reduces the computational complexity while achieving similar estimation performance with the traditional algorithm and also has more prominent advantages in strong clutter environment.

[an error occurred while processing this directive]