文章摘要
李树甫,黄勇,裴家正.基于 CPHD的双层粒子滤波目标跟踪算法[J].,2020,35(4):291-296
基于 CPHD的双层粒子滤波目标跟踪算法
Two-Layer Particle Filter Tracking Algorithm Based on CPHD
  
DOI:10.7682/j.issn.1673-1522.2020.04.002
中文关键词: 多目标跟踪  双层粒子滤波  势概率假设密度  随机有限集
英文关键词: target tracking  two-layer particle filter  cardinalized probability hypothesis density  random finite set
基金项目:
作者单位
李树甫 91049部队,山东青岛 266102 
黄勇 海军航空大学,山东烟台 264001 
裴家正 海军航空大学,山东烟台 264001 
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中文摘要:
      常规基于势概率假设密度滤波(Cardinalized Probability Hypothesis Density,CPHD)的粒子滤波(Particle Fil? ter,PF)跟踪算法应用于多目标跟踪时,容易遇到因粒子数量增加而带来的运算效率下降、目标数目估计不准的问题。文章基于常规粒子滤波 CPHD跟踪算法,通过部署双层粒子,提出基于势概率假设密度滤波的双层粒子滤波 (Two-Layer Particle Filter-CPHD,TLPF-CPHD)算法,以便提高目标数目及状态估计精度。仿真实验结果证明,相比于常规 PF-CPHD算法,新算法具有更好的目标数目和状态估计准确性。
英文摘要:
      When the conventional particle filter (PF) tracking algorithm based on cardinalized probability hypothesis densi?ty (CPHD) is applied to the multi-target tracking, it is easy to encounter the problems of decreased computational efficien?cy and inaccurate estimation of the target number due to the increase in the number of particles. In this paper, based onthe conventional PF-CPHD tracking algorithm, TLPF-CPHD algorithm is proposed to improve the number of targets andthe accuracy of state estimation. The simulation results show that compared with the conventional particle filter tracking al?gorithm based on CPHD, the new algorithm has significant performance advantages in target number and state estimation accuracy.
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