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Robust Device-Free Wireless Localization Based on Differential RSS Measurements

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 a... Full description

Journal Title: IEEE Transactions on Industrial Electronics December 2013, Vol.60(12), pp.5943-5952
Main Author: Jie Wang
Other Authors: Qinghua Gao , Yan Yu , Peng Cheng , Lifei Wu , Hongyu Wang
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0278-0046 ; E-ISSN: 1557-9948 ; DOI: 10.1109/TIE.2012.2228145
Link: https://ieeexplore.ieee.org/document/6355995
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recordid: ieee_s6355995
title: Robust Device-Free Wireless Localization Based on Differential RSS Measurements
format: Article
creator:
  • Jie Wang
  • Qinghua Gao
  • Yan Yu
  • Peng Cheng
  • Lifei Wu
  • Hongyu Wang
subjects:
  • Wireless Communication
  • Robustness
  • Wireless Sensor Networks
  • Shadow Mapping
  • Equations
  • Estimation
  • Mathematical Model
  • Device-Free Localization (Dfl)
  • Differential Received Signal Strength (Rss)
  • Particle Filter (Pf)
  • Wireless Localization
  • Wireless Networks (Wns)
  • Engineering
ispartof: IEEE Transactions on Industrial Electronics, December 2013, Vol.60(12), pp.5943-5952
description: 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.
language: eng
source:
identifier: ISSN: 0278-0046 ; E-ISSN: 1557-9948 ; DOI: 10.1109/TIE.2012.2228145
fulltext: fulltext
issn:
  • 0278-0046
  • 02780046
  • 1557-9948
  • 15579948
url: Link


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subjectWireless Communication ; Robustness ; Wireless Sensor Networks ; Shadow Mapping ; Equations ; Estimation ; Mathematical Model ; Device-Free Localization (Dfl) ; Differential Received Signal Strength (Rss) ; Particle Filter (Pf) ; Wireless Localization ; Wireless Networks (Wns) ; Engineering
descriptionAs 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.
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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.

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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.

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date2013-12