首页> 外文期刊>Wireless personal communications: An Internaional Journal >Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN
【24h】

Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN

机译:室内无线局域网中基于接收信号强度指纹分布位置估计系统的性能分析

获取原文
获取原文并翻译 | 示例
       

摘要

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.
机译:位置估计对于室内无线网络的许多应用已经变得很重要。接收信号强度(RSS)指纹识别方法已被广泛用于位置估计。对于大型站点,大多数位置估计系统都存在可伸缩性和所有位置的所有访问点不可用的问题。估计的准确性和响应时间是大型站点位置估计系统中的关键问题。在本文中,我们提出了一种分布式位置估计方法,该方法将位置估计系统分为子系统。我们的方法根据站点区域各个位置的访问点的可见性,将输入信号空间和输出位置空间划分为群集。输入信号空间的每个群集以及输出位置子空间都用于了解RSS指纹与其在子系统中各自位置之间的关联。我们对两个RSS数据集进行了实验,这两个RSS数据集收集在不同的测试台上,并将我们的结果与基准RADAR方法进行了比较。实验结果表明,与集中式系统(在大型站点中使用单个系统)相比,我们的方法在准确性和响应时间方面提供了更好的结果。

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