RT Journal Article
SR 00
ID 10.1186/1687-6180-2013-16
A1 Savic, Vladimir
A1 Zazo Bello, Santiago
T1 Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks
JF Eurasip Journal on Advances in Signal Processing
YR 2013
FD 2013
VO 2013
IS 16
SP 1
OP 15
AB 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.
PB Springer
SN 1687-6180
LK http://oa.upm.es/28868/
UL http://asp.eurasipjournals.com/content/2013/1/16