个人信息Personal Information
教授级高工
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:控制科学与工程学院
电子邮箱:qhgao@dlut.edu.cn
Robust Device-Free Wireless Localization Based on Differential RSS Measurements
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论文类型:期刊论文
发表时间:2013-12-01
发表刊物:IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
收录刊物:SCIE、EI、Scopus
卷号:60
期号:12
页面范围:5943-5952
ISSN号:0278-0046
关键字:Device-free localization (DFL); differential received signal strength (RSS); particle filter (PF); wireless localization; wireless networks (WNs)
摘要:As an emerging technique with a promising application prospect, the device-free localization (DFL) technique has drawn considerable attention due to its ability of realizing wireless localization without the need of equipping the target with any device. The DFL technique detects the shadowed links and realizes localization with the received signal strength (RSS) measurements of these links. However, one major disadvantage of the DFL technique is that the RSS signal is too sensitive, and a slight variation of the environment will cause the variation of RSS measurements, which incurs the misjudgment of shadowed links and degradation of localization performance. To solve this problem, a robust DFL scheme based on differential RSS is proposed. The scheme utilizes the novel differential RSS to judge whether a link is shadowed, which not only eliminates the need of acquiring reference RSS measurements but also overcomes the negative effect incurred by the environment. Meanwhile, an outlier detection scheme is presented to filter out the outlier links that are far away from the target. We present the observation model of the shadowed links and incorporate it into the particle filter framework to realize location estimation robustly. Experimental results demonstrate the outstanding performance of the proposed scheme.