The Application of AC-GA on Multi-Sensor Multi-Target Tracking

YUAN Shu; YUAN Dong-hui; SUN Ji-zhou; LIU Yong-bo; LI Jing; YUAN Lin

doi:10.3969/j.issn.0372-2112.2013.03.032

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The Application of AC-GA on Multi-Sensor Multi-Target Tracking

更新时间：2025-07-16

- The Application of AC-GA on Multi-Sensor Multi-Target Tracking
- Acta Electronica Sinica Vol. 41, Issue 3, Pages: 609-614(2013)
- 作者机构：
  
  1. 吉林工商学院,吉林,长春,130062
  2. 空军哈尔滨飞行学院,黑龙江,哈尔滨,150001
  3. 吉林大学知识工程与符号计算教育部重点实验室,吉林,长春,130012
  4. 吉林工商学院,吉林,长春,130062
  5. 空军哈尔滨飞行学院,黑龙江,哈尔滨,150001
  6. 吉林大学知识工程与符号计算教育部重点实验室,吉林,长春,130012
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.03.032
  CLC： TP391
- Published：2013
- 稿件说明：
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YUAN Shu, YUAN Dong-hui, SUN Ji-zhou, et al. The Application of AC-GA on Multi-Sensor Multi-Target Tracking[J]. Acta Electronica Sinica, 2013, 41(3): 609-614.
DOI：

YUAN Shu, YUAN Dong-hui, SUN Ji-zhou, et al. The Application of AC-GA on Multi-Sensor Multi-Target Tracking[J]. Acta Electronica Sinica, 2013, 41(3): 609-614. DOI： 10.3969/j.issn.0372-2112.2013.03.032.

摘要

本文提出了一种高效的多目标数据关联算法AC-GADA(Ant Colony-Genetic Algorithm Data Association)

该算法以蚁群、遗传算法为基础

利用种群差异性使个体携带信息素

构建了全局信息素扩散模型

并引入了交叉变异策略和种群适应度模型.通过大量的实验数据证明

该算法在获得较高关联准确率的同时可以有效地提高关联速度.

Abstract

For the application of multi-sensor multi-target tracking

a method of data association based on ant colony algorithm and genetic algorithm is proposed in this study.First

this method definites pheromone differently for each independent ant entity.Then

improved global pheromone increment model

and combined crossover and variation operation with fitness model of population.The experimental results of actual data demonstrate the presented algorithm is effective.

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