hkr.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Localizing multiple objects using radio tomographic imaging technology
Högskolan Kristianstad, Sektionen för hälsa och samhälle, Avdelningen för Design och datavetenskap.ORCID-id: 0000-0002-8032-6291
Finland.
Kina.
Finland.
2016 (Engelska)Ingår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, nr 5, s. 3641-3656Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Low data rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio frequency magnetic field, and can be detected by observing the change of radio attenuation. This gives the basis for the radio tomographic imaging technology which has been recently developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multi-object intrusion detection and localization is a challenging issue. This article proposes an extended VB-GMM (i.e. variational Bayesian Gaussian mixture model) algorithm in treating this problem. The extended VBGMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects, and uses a modified version of the variational Bayesian approach for model estimation. Real world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multi-object localization algorithm.

Ort, förlag, år, upplaga, sidor
2016. Vol. 65, nr 5, s. 3641-3656
Nyckelord [en]
Gaussian mixture model, multiple object localization, physical intrusion detection, radio tomographic imaging, variational Bayesian
Nationell ämneskategori
Inbäddad systemteknik
Identifikatorer
URN: urn:nbn:se:hkr:diva-14043DOI: 10.1109/TVT.2015.2432038ISI: 000376094500060OAI: oai:DiVA.org:hkr-14043DiVA, id: diva2:822160
Tillgänglig från: 2015-06-16 Skapad: 2015-06-16 Senast uppdaterad: 2017-12-04Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Person

Wang, Qinghua

Sök vidare i DiVA

Av författaren/redaktören
Wang, Qinghua
Av organisationen
Avdelningen för Design och datavetenskap
I samma tidskrift
IEEE Transactions on Vehicular Technology
Inbäddad systemteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 309 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf