Memorias de investigación
Research Publications in journals:
Nonparametric Generalized Belief Propagation based on Pseudo-Junction Tree for Cooperative Localization in Wireless Networks
Year:2013
Research Areas
  • Electronic technology and of the communications
Information
Abstract
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.
International
Si
JCR
Si
Title
Eurasip Journal on Advances in Signal Processing
ISBN
1687-6180
Impact factor JCR
0,807
Impact info
Volume
Journal number
From page
1
To page
15
Month
SIN MES
Ranking
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Aplicaciones del Procesado de Señal (GAPS)
  • Departamento: Señales, Sistemas y Radiocomunicaciones