Distributed clustering fusion algorithm based on EK-GMPHD

DONG Qing; HU Jian-wang; JI Bing

doi:10.13873/J.1000-9787(2019)01-0134-03

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Distributed clustering fusion algorithm based on EK-GMPHD

更新时间：2025-09-11

- Distributed clustering fusion algorithm based on EK-GMPHD
- Vol. 38, Issue 1, Pages: 134-136+140(2019)
- 作者机构：
  
  陆军工程大学石家庄校区信息工程系
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2019)01-0134-03
  CLC： TP212
- Published：2019
- 稿件说明：
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[1]董青,胡建旺,吉兵.基于EK-GMPHD的分布式聚类融合算法[J].传感器与微系统,2019,38(01):134-136+140.

DONG Qing, HU Jian-wang, JI Bing. Distributed clustering fusion algorithm based on EK-GMPHD[J]. 2019, 38(1): 134-136+140.
[1]董青,胡建旺,吉兵.基于EK-GMPHD的分布式聚类融合算法[J].传感器与微系统,2019,38(01):134-136+140. DOI： 10.13873/J.1000-9787(2019)01-0134-03.

DONG Qing, HU Jian-wang, JI Bing. Distributed clustering fusion algorithm based on EK-GMPHD[J]. 2019, 38(1): 134-136+140. DOI： 10.13873/J.1000-9787(2019)01-0134-03.

摘要

针对杂波环境下非线性系统中目标跟踪精度问题

提出了一种基于扩展卡尔曼—高斯混合概率假设密度（EK-GMPHD）的模糊C均值（FCM）分布式融合算法。算法在全局估计上采用FCM聚类融合算法

得出隶属度最大的目标状态估计

再根据相关公式

计算得出全局目标状态估计。在局部航迹缺乏的状态下

所提算法可提高目标的跟踪精度。仿真结果表明:提出的融合算法在杂波环境下对多目标进行跟踪时对多目标数目和状态的估计更优。

Abstract

Aiming at the problem of target tracking precision in nonlinear system in clutter environment

a fuzzy C-means（ FCM） distributed fusion algorithm is proposed based on extended Kalman-Gaussian mixture probability hypothesis density（ EK-GMPHD）. The FCM clustering fusion algorithm is used in global estimation

then the maximum target state estimation of membership degree is obtained

and according to related formula

calculate and derive the global target state estimation. The algorithm can improve target tracking precision in condition of lack of local tracks. Simulation results show that the fusion algorithm for multi-target tracking estimation of the number of multiple objective and state is better in clutter environment.

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references

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