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Savic
Vladimir

Zazo Bello
Santiago
Nonparametric generalized belief propagation based on pseudojunction tree for cooperative localization in wireless networks
Springer
byncnd
pub
 telecomunicaciones
public
Nonparametric belief propagation (NBP) is a wellknown message passing method for cooperative localization in wireless networks. However, due to the overcounting problem in the networks with loops, NBP’s convergence is not guaranteed, and its estimates are typically less accurate. One solution for this problem is nonparametric generalized belief propagation based on junction tree. However, this method is intractable in largescale networks due to the highcomplexity of the junction tree formation, and the highdimensionality of the particles. Therefore, in this article, we propose the nonparametric generalized belief propagation based on pseudojunction tree (NGBPPJT). The main difference comparing with the standard method is the formation of pseudojunction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of highdimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As byproduct, we also propose NBP based on thin graph (NBPTG), a cheaper variant of NBP, which runs on the same graph as NGBPPJT. According to our simulation and experimental results, NGBPPJT method outperforms NBP and NBPTG in terms of accuracy, computational, and communication cost in reasonably sized networks.
published
2013
Eurasip Journal on Advances in Signal Processing
2013
16
115
10.1186/16876180201316
Telecomunicacion
Senales
TRUE
16876180
http://asp.eurasipjournals.com/content/2013/1/16