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Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network
Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) senso...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5464195/ https://www.ncbi.nlm.nih.gov/pubmed/28448464 http://dx.doi.org/10.3390/s17050969 |
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author | Wang, Zhenghuan Liu, Heng Xu, Shengxin Bu, Xiangyuan An, Jianping |
author_facet | Wang, Zhenghuan Liu, Heng Xu, Shengxin Bu, Xiangyuan An, Jianping |
author_sort | Wang, Zhenghuan |
collection | PubMed |
description | Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably. |
format | Online Article Text |
id | pubmed-5464195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54641952017-06-16 Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network Wang, Zhenghuan Liu, Heng Xu, Shengxin Bu, Xiangyuan An, Jianping Sensors (Basel) Article Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably. MDPI 2017-04-27 /pmc/articles/PMC5464195/ /pubmed/28448464 http://dx.doi.org/10.3390/s17050969 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Zhenghuan Liu, Heng Xu, Shengxin Bu, Xiangyuan An, Jianping Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network |
title | Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network |
title_full | Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network |
title_fullStr | Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network |
title_full_unstemmed | Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network |
title_short | Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network |
title_sort | bayesian device-free localization and tracking in a binary rf sensor network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5464195/ https://www.ncbi.nlm.nih.gov/pubmed/28448464 http://dx.doi.org/10.3390/s17050969 |
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