liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
Technical University of Madrid, Spain. (Signal Processing Applications Group)
2013 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 16Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 16
Keyword [en]
nonparametric belief propagation, cooperative localization, wireless sensor networks, junction tree
National Category
Signal Processing Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-88543DOI: 10.1186/1687-6180-2013-16ISI: 000317675200002OAI: oai:DiVA.org:liu-88543DiVA: diva2:604724
Projects
COOPLOCFP7-ICT WHERE2
Funder
Swedish Foundation for Strategic Research
Available from: 2013-02-12 Created: 2013-02-12 Last updated: 2017-12-06

Open Access in DiVA

fulltext(942 kB)214 downloads
File information
File name FULLTEXT01.pdfFile size 942 kBChecksum SHA-512
05f4bfec7914e899ebbc0392a17e9617e767f9ef52d61d0f5f49335ae232a6186f275d5e0a6998ffe4a207ac2096987bfebe70a68c9ccac6a0c5969eeffbbfeb
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Savic, Vladimir

Search in DiVA

By author/editor
Savic, Vladimir
By organisation
Communication SystemsThe Institute of Technology
In the same journal
EURASIP Journal on Advances in Signal Processing
Signal ProcessingCommunication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 214 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 463 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf