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Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

Non-parametric belief propagation (NBP) is a well-known message passing method for cooperative localization in wireless networks. However, due to the over-counting 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 non-parametric generalized belief propagation based on junction tree. However, this method is intractable in large-scale networks due to the high-complexity of the junction tree formation, and the high-dimensionality of the particles. Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT). The main difference comparing with the standard method is the formation of pseudo-junction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of high-dimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As by-product, we also propose NBP based on thin graph (NBP-TG), a cheaper variant of NBP, which runs on the same graph as NGBP-PJT. According to our simulation and experimental results, NGBP-PJT method outperforms NBP and NBP-TG in terms of accuracy, computational, and communication cost in reasonably sized networks. / COOPLOC / FP7-ICT WHERE2

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-88543
Date January 2013
CreatorsSavic, Vladimir, Zazo, Santiago
PublisherLinköpings universitet, Kommunikationssystem, Linköpings universitet, Tekniska högskolan, Technical University of Madrid, Spain
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeArticle in journal, info:eu-repo/semantics/article, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationEURASIP Journal on Advances in Signal Processing, 1687-6172, 2013, 16,

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