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Localizing multiple objects using radio tomographic imaging technology
Kristianstad University, School of Health and Society, Avdelningen för Design och datavetenskap.ORCID iD: 0000-0002-8032-6291
Finland.
Kina.
Finland.
2016 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 5, 3641-3656 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2016. Vol. 65, no 5, 3641-3656 p.
Keyword [en]
Gaussian mixture model, multiple object localization, physical intrusion detection, radio tomographic imaging, variational Bayesian
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:hkr:diva-14043DOI: 10.1109/TVT.2015.2432038ISI: 000376094500060OAI: oai:DiVA.org:hkr-14043DiVA: diva2:822160
Available from: 2015-06-16 Created: 2015-06-16 Last updated: 2017-02-22Bibliographically approved

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Wang, Qinghua
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Avdelningen för Design och datavetenskap
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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