首页> 外文会议>IEEE Global Communications Conference >Robust Indoor Localization based on Hybrid Bayesian Graphical Models
【24h】

Robust Indoor Localization based on Hybrid Bayesian Graphical Models

机译:基于混合贝叶斯图形模型的强大室内定位

获取原文
获取外文期刊封面目录资料

摘要

In this paper, we study the problem of accurately estimating the location of indoor wireless devices which, due to its vast application areas, has continued to generate much interest both in the academia and industry. Specifically, we consider the location estimation problem and develop non-hybrid Bayesian location estimators for when received signal strength (RSS) and/or time of arrival (TOA) measurements between the target node (TN) and designated beacon nodes (BNs) in the network are obtainable. However, RSS- and TOA-based location estimators can be inaccurate when the RF-characteristics are not stable and time synchronization between the TN and BNs is not set up properly, respectively. To mitigate the disadvantages of these two location estimators, we propose the hybrid Bayesian location estimators. We show, through the results of extensive simulation experiments conducted that in comparison with the non-hybrid estimators, the hybrid Bayesian location estimators are more robust in the localization environment where RSS/TOA measurement precision varies.
机译:在本文中,我们研究了准确估计室内无线设备的位置的问题,由于其庞大的应用领域,在学术界和工业中继续产生很多兴趣。具体地,我们考虑位置估计问题,并开发非混合贝叶斯定位估计,用于当接收的信号强度(RSS)和/或到达时间(TOA)测量在目标节点(TN)和指定的信标节点(BNS)之间网络是可获得的。然而,当RF-特性不稳定并且TN和BNS之间的时间同步不正确地设置RFS和TOA的位置估计器可以不准确。为了减轻这两个位置估计器的缺点,我们提出了混合贝叶斯定位估计。我们展示了广泛的仿真实验结果,与非混合估计器相比,混合贝叶斯定位估计器在定位环境中更加强大,其中RSS / TOA测量精度变化。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号