1 Ocak 2021 Konferans bildirisi Açık Erişim

Kaya, Engin; Kaplan, Gulay Buyukaksoy

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/240070</identifier>
  <creators>
    <creator>
      <creatorName>Kaya, Engin</creatorName>
      <givenName>Engin</givenName>
      <familyName>Kaya</familyName>
      <affiliation>TUBITAK BILGEM, Informat Technol Inst, TR-41400 Kocaeli, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kaplan, Gulay Buyukaksoy</creatorName>
      <givenName>Gulay Buyukaksoy</givenName>
      <familyName>Kaplan</familyName>
      <affiliation>TUBITAK BILGEM, Informat Technol Inst, TR-41400 Kocaeli, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Neural Network Based Drone Recognition Techniques With Non-Coherent S-Band Radar</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/240070</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/RadarConf2147009.2021.9455167</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Increasing drone accidents and abuse has led to the development of drone detection systems. In this study we proposed two classification approaches for the recognition of flying drones using non-coherent S-band radar. The radar data including drone, ship and bird targets is collected in various scenarios. While both of the proposed classification methods utilize neural networks, the first one is trained with the features extracted from track information. The second technique is based on radar images, meaning that video classification methods are employed. The results were investigated with respect to the correct classification performance and false alarm rate. Experimental results have shown that the image based method is better at recognizing not only drone but also birds and ships with a lower false alarm rate.</description>
  </descriptions>
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