Node localization based on SLAM-PSO algorithm

ZHANG Guang-feng; DUAN Qi-chang; LIU Zheng

doi:10.13873/j.1000-97872013.03.036

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Node localization based on SLAM-PSO algorithm

更新时间：2025-09-11

- Node localization based on SLAM-PSO algorithm
- Vol. 32, Issue 3, Pages: 72-73+77(2013)
- 作者机构：
  
  重庆大学自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872013.03.036
  CLC： TP18
- Published：2013
- 稿件说明：
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[1]张广峰,段其昌,刘政.基于加强学习与联想记忆粒子群优化算法的节点定位[J].传感器与微系统,2013,32(03):72-73+77.

ZHANG Guang-feng, DUAN Qi-chang, LIU Zheng. Node localization based on SLAM-PSO algorithm[J]. 2013, 32(3): 72-73+77.
[1]张广峰,段其昌,刘政.基于加强学习与联想记忆粒子群优化算法的节点定位[J].传感器与微系统,2013,32(03):72-73+77. DOI： 10.13873/j.1000-97872013.03.036.

ZHANG Guang-feng, DUAN Qi-chang, LIU Zheng. Node localization based on SLAM-PSO algorithm[J]. 2013, 32(3): 72-73+77. DOI： 10.13873/j.1000-97872013.03.036.

摘要

提出了加强学习与联想记忆的粒子群优化算法

并将该算法应用到无线传感器网络的节点定位中。在RSSI模型测距产生的不同误差情况下

分别比较极大似然估计法和加强学习与联想记忆的粒子群优化算法产生的定位误差、定位方差

证明了加强学习与联想记忆的粒子群优化算法是一种收敛快、精度高、稳定性好的优化算法

适合应用在无线传感器网络节点定位中。

Abstract

A strengthen learning and associative memory particle swarm optimization（SLAM-PSO）algorithm is presented

and the algorithm is applied to node localization in wireless sensor networks（WSNs）.With different ranging error based on RSSI model

comparing the localization error and localization variance generated by maximum likelihood estimation method and SLAM-PSO algorithm

and it proves that the SLAM-PSO algorithm is a fast convergence

high precision

good stability optimization algorithm

and it is suitable for application in WSNs node positioning.

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references

基于量子行为粒子群优化算法的定位技术研究 [J]. 赵吉,纪志成. 传感器与微系统 . 2012(05)

基于粒子群优化算法的WSNs节点定位研究 [J]. 王晓乐,徐家品. 计算机应用 . 2009(02)

粒子群优化算法中加速因子的设置与试验分析 [J]. 冯翔,陈国龙,郭文忠. 集美大学学报(自然科学版) . 2006(02)

粒子群优化算法的惯性权值递减策略研究 [J]. 陈贵敏,贾建援,韩琪. 西安交通大学学报 . 2006(01)

无线传感器网络[M]. 清华大学出版社 , 孙利民等编著, 2005

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