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Micro-doppler classification with boosting in perimeter protection
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0003-0701-706x
Totalforsvarets forskningsinstitut, SWE.
2017 (English)In: IET Conference Publications, Institution of Engineering and Technology , 2017, no CP728Conference paper, Published paper (Refereed)
Abstract [en]

In security surveillance at the perimeter of critical infrastructure, such as airports and power plants, approaching objects have to be detected and classified. Especially important is to distinguish between humans, animals and vehicles. In this paper, micro-Doppler data (from movement of internal parts of the target) have been collected with a small radar. From time-velocity diagrams of the data, physical features have been extracted and used in a Boosting classifier to distinguish between the classes "human", "animal" and "man-made object". This type of classifier has received much attention lately, but not in radar micro-Doppler classification. The classification result on the current data reaches 90% correct classification with this classifier. The ability to distinguish between humans and animals is good on this data. This classifier type gives insight into the classifier and the utilized features, and is easy to use. A comparison with a SVM (Support Vector Machine) classifier, which is common for micro-Doppler, has also been performed. © 2017 Institution of Engineering and Technology. All rights reserved.

Place, publisher, year, edition, pages
Institution of Engineering and Technology , 2017. no CP728
Keywords [en]
Boosting, Classification, Micro-doppler, Radar, Airport security, Animals, Object detection, Radar systems, Support vector machines, Boosting classifiers, Classification results, Perimeter protection, Physical features, Security surveillance, SVM(support vector machine), Classification (of information)
National Category
Signal Processing Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-16646Scopus ID: 2-s2.0-85048690177OAI: oai:DiVA.org:bth-16646DiVA, id: diva2:1228518
Conference
2017 International Conference on Radar Systems, Radar 2017, Belfast
Available from: 2018-06-28 Created: 2018-06-28 Last updated: 2018-06-29Bibliographically approved

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Björklund, Svante

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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