基于RBF神经网络的室内定位算法研究
DOI:
作者:
作者单位:

电子工程学院合肥230037

作者简介:

通讯作者:

中图分类号:

TP393

基金项目:


Research on indoor location algorithm based on RBF neural network
Author:
Affiliation:

Electronic Engineering Institute, Hefei 230037, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    在无线传感器网络室内定位中,由于遮挡、多径效应等因素的影响,传统基于RSSI(Received Signal Strength Indicator)的定位算法存在测距不准、定位精度不高的问题。针对此问题,本文提出一种改进的基于RBF(Radial Basis Function)神经网络的室内定位算法,算法在离线阶段直接建立各参考节点接收到的RSSI值与其位置坐标的映射关系;在线阶段采集待定位节点的RSSI值,利用学习好的神经网络对待定位节点进行定位。实验结果表明,与传统RSSI定位算法相比,本文提出的定位算法具备更高的定位精度。

    Abstract:

    Located in indoor environment of wireless sensor network, the traditional RSSI (Received Signal Strength Indicator) localization algorithm has the shortcomings of inaccurate distance measurement and imprecise location because of the influence of shelter and the multipath effect. Aimed to solve the problem, a RSSI localization algorithm used RBF (radial basis function) neural network is proposed. Offline stage, the mapping relation between the RSSI value that the reference node received and its spatial coordinate is established. Online stage, the RSSI value is collected and the well trained neural network is performed to locate the node without the known orientation. The experimental results show that the proposed algorithm can effectively improve the positioning accuracy compared with the traditional RSSI localization algorithm.

    参考文献
    相似文献
    引证文献
引用本文

龚阳,崔琛,余剑,孙从易.基于RBF神经网络的室内定位算法研究[J].电子测量技术,2016,39(10):57-60

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2016-11-24
  • 出版日期: