Application of improved particle swarm optimization algorithm in TDOA positioning

ZHAI Yan-rong; HUANG Huan; ZHANG Shen; MA Xiu-ping; LIU Wei; YOU Chun-xia

doi:10.13873/j.1000-97872013.04.021

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Application of improved particle swarm optimization algorithm in TDOA positioning

更新时间：2025-09-11

- Application of improved particle swarm optimization algorithm in TDOA positioning
- Vol. 32, Issue 4, Pages: 145-148+152(2013)
- 作者机构：
  
  中国矿业大学物联网(感知矿山)研究中心
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872013.04.021
  CLC： TN95;TP301.6
- Published：2013
- 稿件说明：
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[1]翟彦蓉,黄欢,张申,马秀萍,刘伟,游春霞.改进粒子群优化算法在TDOA定位中的应用[J].传感器与微系统,2013,32(04):145-148+152.

ZHAI Yan-rong, HUANG Huan, ZHANG Shen, et al. Application of improved particle swarm optimization algorithm in TDOA positioning[J]. 2013, 32(4): 145-148+152.
[1]翟彦蓉,黄欢,张申,马秀萍,刘伟,游春霞.改进粒子群优化算法在TDOA定位中的应用[J].传感器与微系统,2013,32(04):145-148+152. DOI： 10.13873/j.1000-97872013.04.021.

ZHAI Yan-rong, HUANG Huan, ZHANG Shen, et al. Application of improved particle swarm optimization algorithm in TDOA positioning[J]. 2013, 32(4): 145-148+152. DOI： 10.13873/j.1000-97872013.04.021.

摘要

针对TDOA定位估计中的非线性最优化问题

提出了一种基于改进粒子群优化的TDOA定位算法。该算法在自适应粒子群优化算法的基础上

引入禁忌搜索策略

有效地解决了粒子群优化算法容易陷入局部最优的问题

使算法快速收敛到全局最优解。仿真结果表明:该算法性能稳定

定位精度高。

Abstract

A new time-difference of arrival（TDOA） positioning algorithm based on improved particle swarm optimization（PSO） is proposed for nonlinear optimization of TDOA positioning estimation.The algorithm uses tabu search strategy based on adaptive PSO algorithm

effectively resolves the problem that PSO algorithm easy to fall into local optimum and the proposed algorithm converges to global optimum fastly.Simulation results verify that the algorithm performs stably with high positioning precision.

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references

基于泰勒级数展开的蜂窝TDOA定位新算法 [J]. 张令文,谈振辉. 通信学报 . 2007(06)

一种动态改变惯性权的自适应粒子群算法 [J]. 张选平,杜玉平,秦国强,覃征. 西安交通大学学报 . 2005(10)

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