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Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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Academic Editors: Jesús Ureña, Álvaro Hernández Alonso and Juan Jesús García Domínguez
Sensors 2017, 17(5), 969; https://doi.org/10.3390/s17050969
Received: 18 January 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 27 April 2017
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. View Full-Text
Keywords: device-free localization (DFL); RSS; energy efficiency; maximum likelihood; particle filter device-free localization (DFL); RSS; energy efficiency; maximum likelihood; particle filter
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MDPI and ACS Style

Wang, Z.; Liu, H.; Xu, S.; Bu, X.; An, J. Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network. Sensors 2017, 17, 969. https://doi.org/10.3390/s17050969

AMA Style

Wang Z, Liu H, Xu S, Bu X, An J. Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network. Sensors. 2017; 17(5):969. https://doi.org/10.3390/s17050969

Chicago/Turabian Style

Wang, Zhenghuan, Heng Liu, Shengxin Xu, Xiangyuan Bu, and Jianping An. 2017. "Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network" Sensors 17, no. 5: 969. https://doi.org/10.3390/s17050969

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