吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (4): 1191-1196.doi: 10.13229/j.cnki.jdxbgxb201404044

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自适应蒙特卡罗无线传感器网络移动节点定位算法

李建坡, 时明, 钟鑫鑫   

  1. 东北电力大学 信息工程学院, 吉林 吉林132012
  • 收稿日期:2013-01-30 出版日期:2014-07-01 发布日期:2014-07-01
  • 作者简介:李建坡(1980-), 男, 副教授, 博士.研究方向:无线传感器网络.E-mail:jianpoli@163.com
  • 基金资助:
    国家自然科学基金项目(51077010); 国家留学基金项目(留金发\[2012\]3043); 吉林省教育厅“十一五”科学技术研究项目(吉教科合字\[2009\]第101号); 东北电力大学青年学术骨干科研促进计划项目

Self-adaptive Monte Carlo localization algorithm of mobile nodes in WSN

LI Jian-po, SHI Ming, ZHONG Xin-xin   

  1. School of Information Engineering, Northeast Dianli University, Jilin 132012, China
  • Received:2013-01-30 Online:2014-07-01 Published:2014-07-01

摘要: 针对无线传感器网络中蒙特卡罗定位算法在节点的无线射程为非理想条件下定位精度不高、采样率低等缺点, 提出一种自适应蒙特卡罗移动节点定位算法。该算法利用不同区域的采样粒子对未知节点的定位精度影响不同, 自适应地调整不同区域的采样粒子的影响权重, 对未知节点进行定位;同时, 利用上一时刻采样粒子增加限定条件, 提高定位精度。仿真结果表明, 本算法在规则度不同的条件下节点的定位误差平均下降了约13%, 在速度不同的条件下定位误差平均下降了约10%, 网络覆盖率可达到99.19%。

关键词: 通信技术, 移动节点定位, 蒙特卡罗定位算法, 无线传感器网络

Abstract: To overcome the disadvantages of low localization accuracy and poor efficiency of Monte Carlo Localization (MCL) algorithm in harsh Wireless Sensor Network (WSN), a self-adaptive localization algorithm based on MCL is proposed. The sample particles in different regions have different effects on unknown node localization accuracy. The proposed algorithm assigns self-adaptive weights to sample particles in different regions to position the unknown nodes. At the same time, the algorithm adds the constraint condition using the last-time sample particles. Simulation results show that the average localization error of the proposed self-adaptive MCL algorithm descends 13% at different degrees of irregularity. The average localization error descends about 10% at different node speeds. The network coverage rate reaches 99.19%.

Key words: communication, mobile node localization, Monte Carlo localization(MCL) algorithm, wireless sensor network

中图分类号: 

  • TN911
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