DocumentCode
624586
Title
An Exponential-Rayleigh signal strength model for device-free localization and tracking with wireless networks
Author
Yao Guo ; Kaide Huang ; Nanyong Jiang ; Xuemei Guo ; Guoli Wang
Author_Institution
Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China
fYear
2013
fDate
9-11 June 2013
Firstpage
108
Lastpage
113
Abstract
We present a new statistic signal strength model, called Exponential-Rayleigh (ER) model, for device-free (DF) target localization and tracking issues in this paper. It is a single target measurement model for radio frequency (RF) based on received signal strength (RSS) measurement in outdoor regions. The model is a non-linear function between RSS measurements and target motion state. It consists of three parts: the largescale exponential attenuation part, the small-scale Rayleigh enhancement part and the noise. Different from the proposed models, while reserving the large-scale attenuation, we mainly present the small-scale Rayleigh enhancement model in ER model. The Rayleigh part depicts the multi-path caused by single target so as to reduce the multi-path error. In the context of localization and tracking experiment using particle filter, we validate the effectiveness of ER model.
Keywords
particle filtering (numerical methods); radio networks; target tracking; DF target localization; ER model; RF; RSS measurement; device-free localization; device-free tracking; exponential-Rayleigh signal strength model; large-scale exponential attenuation; multipath error; nonlinear function; particle filter; radio frequency; received signal strength; statistic signal strength model; target measurement model; target motion state; wireless network; Attenuation; Data models; Erbium; Mathematical model; Radio frequency; Sensors; Target tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4673-6248-1
Type
conf
DOI
10.1109/ICICIP.2013.6568050
Filename
6568050
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