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Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In par...

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Autores principales: Zhou, Bingpeng, Chen, Qingchun, Li, Tiffany Jing, Xiao, Pei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279534/
https://www.ncbi.nlm.nih.gov/pubmed/25393784
http://dx.doi.org/10.3390/s141121281
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author Zhou, Bingpeng
Chen, Qingchun
Li, Tiffany Jing
Xiao, Pei
author_facet Zhou, Bingpeng
Chen, Qingchun
Li, Tiffany Jing
Xiao, Pei
author_sort Zhou, Bingpeng
collection PubMed
description The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.
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spelling pubmed-42795342015-01-15 Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks Zhou, Bingpeng Chen, Qingchun Li, Tiffany Jing Xiao, Pei Sensors (Basel) Article The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying. MDPI 2014-11-11 /pmc/articles/PMC4279534/ /pubmed/25393784 http://dx.doi.org/10.3390/s141121281 Text en © 2014 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 license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhou, Bingpeng
Chen, Qingchun
Li, Tiffany Jing
Xiao, Pei
Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks
title Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks
title_full Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks
title_fullStr Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks
title_full_unstemmed Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks
title_short Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks
title_sort online variational bayesian filtering-based mobile target tracking in wireless sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279534/
https://www.ncbi.nlm.nih.gov/pubmed/25393784
http://dx.doi.org/10.3390/s141121281
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