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Decentralized Variational Filtering for Target Tracking in Binary Sensor Networks
October 2010 (vol. 9 no. 10)
pp. 1465-1477
ASCII Text
x 
 
Jing Teng, Hichem Snoussi, Cédric Richard, "Decentralized Variational Filtering for Target Tracking in Binary Sensor Networks," IEEE Transactions on Mobile Computing, vol. 9, no. 10, pp. 1465-1477, October, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TMC.2010.117,
author = {Jing Teng and Hichem Snoussi and Cédric Richard},
title = {Decentralized Variational Filtering for Target Tracking in Binary Sensor Networks},
journal ={IEEE Transactions on Mobile Computing},
volume = {9},
number = {10},
issn = {1536-1233},
year = {2010},
pages = {1465-1477},
doi = {http://doi.ieeecomputersociety.org/10.1109/TMC.2010.117},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Mobile Computing
TI - Decentralized Variational Filtering for Target Tracking in Binary Sensor Networks
IS - 10
SN - 1536-1233
SP1465
EP1477
EPD - 1465-1477
A1 - Jing Teng,
A1 - Hichem Snoussi,
A1 - Cédric Richard,
PY - 2010
KW - Variational methods
KW - Bayesian inference
KW - sensor networks
KW - Monte Carlo methods.
VL - 9
JA - IEEE Transactions on Mobile Computing
ER -
 
Jing Teng, North China Electric Power University, Beijing, China
Hichem Snoussi, University of Technology of Troyes, Troyes, France
Cédric Richard, University of Nice Sophia-Antipolis, France
The prime motivation of our work is to balance the inherent trade-off between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through three phases. First, a cluster-based scheme is exploited. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster, we propose a nonmyopic rule, which is based on not only the target state prediction but also its future tendency. Second, the variational filtering algorithm is capable of precise tracking even in the highly nonlinear case. Furthermore, since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved. The intercluster information exchange is thus reduced to one single Gaussian statistic, dramatically cutting down the resource consumption. Third, a binary proximity observation model is employed by the activated slave sensors to reduce the energy consumption and to minimize the intracluster communication. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art algorithms in terms of tracking accuracy, internode communication, and computation complexity.

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Index Terms:
Variational methods, Bayesian inference, sensor networks, Monte Carlo methods.
Citation:
Jing Teng, Hichem Snoussi, Cédric Richard, "Decentralized Variational Filtering for Target Tracking in Binary Sensor Networks," IEEE Transactions on Mobile Computing, vol. 9, no. 10, pp. 1465-1477, Oct. 2010, doi:10.1109/TMC.2010.117
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