Conference paper Open Access

UAV Classification With Deep Learning Using Surveillance Radar Data

Stamatios Samaras; Vasileios Magoulianitis; Anastasios Dimou; Dimitrios Zarpalas; Petros Daras


DCAT Export

<?xml version='1.0' encoding='utf-8'?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#">
  <rdf:Description rdf:about="https://zenodo.org/record/3582010">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3582010</dct:identifier>
    <foaf:page rdf:resource="https://zenodo.org/record/3582010"/>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Stamatios Samaras</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>ITI CERTH</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Vasileios Magoulianitis</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>ITI CERTH</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description rdf:about="http://orcid.org/0000-0002-3950-3305">
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0002-3950-3305</dct:identifier>
        <foaf:name>Anastasios Dimou</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>ITI CERTH</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description rdf:about="http://orcid.org/0000-0002-9649-9306">
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0002-9649-9306</dct:identifier>
        <foaf:name>Dimitrios Zarpalas</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>ITI CERTH</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description rdf:about="http://orcid.org/0000-0003-3814-6710">
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0003-3814-6710</dct:identifier>
        <foaf:name>Petros Daras</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>ITI CERTH</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:title>UAV Classification With Deep Learning Using Surveillance Radar Data</dct:title>
    <dct:publisher>
      <foaf:Agent>
        <foaf:name>Zenodo</foaf:name>
      </foaf:Agent>
    </dct:publisher>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>UAVs</dcat:keyword>
    <dcat:keyword>Drones</dcat:keyword>
    <dcat:keyword>Classification</dcat:keyword>
    <dcat:keyword>Deep learning</dcat:keyword>
    <dcat:keyword>Surveillance radar</dcat:keyword>
    <frapo:isFundedBy rdf:resource="info:eu-repo/grantAgreement/EC/H2020/740859/"/>
    <schema:funder>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </schema:funder>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-11-23</dct:issued>
    <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/>
    <owl:sameAs rdf:resource="https://zenodo.org/record/3582010"/>
    <adms:identifier>
      <adms:Identifier>
        <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3582010</skos:notation>
        <adms:schemeAgency>url</adms:schemeAgency>
      </adms:Identifier>
    </adms:identifier>
    <owl:sameAs rdf:resource="https://doi.org/10.1007/978-3-030-34995-0_68"/>
    <dct:description>&lt;p&gt;The Unmanned Aerial Vehicle (UAV) proliferation has raised many concerns, since their potentially malicious usage renders them as a detrimental tool for a number of illegal activities. Radar based counterUAV applications provide a robust solution for UAV detection and classification. Most of the existing research addresses the problem of UAV classification by extracting features from the time variations of the Fourier spectra. Yet, these solutions require that the UAV is illuminated by the radar for a longer time which can be only met by a tracking radar architecture. On the other hand, surveillance radar architectures don&amp;rsquo;t have such a cumbersome requirement and are generally superior in maintaining situational awareness, due their ability for constantly searching on a 360◦ area for targets. Nevertheless, the available automatic UAV classification methods for this type of radar sensors are relatively inefficient. This work proposes the incorporation of the deep learning paradigm in the classification pipeline, to provide an alternative UAV classification method that can handle data from a surveillance radar. Therefore, a Deep Neural Network (DNN) model is employed to discern between UAVs and negative examples (e.g. birds, noise, etc.). The conducted experiments demonstrate the validity of the proposed method, where the overall classification accuracy can reach up to 95.0%.&lt;/p&gt;</dct:description>
    <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/>
    <dct:accessRights>
      <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess">
        <rdfs:label>Open Access</rdfs:label>
      </dct:RightsStatement>
    </dct:accessRights>
    <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL rdf:resource="https://doi.org/10.1007/978-3-030-34995-0_68"/>
        <dcat:byteSize>994036</dcat:byteSize>
        <dcat:downloadURL rdf:resource="https://zenodo.org/record/3582010/files/UAV4S_UAV Classification With Deep Learning Using Surveillance Radar Data_camera_ready.pdf"/>
        <dcat:mediaType>application/pdf</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
  </rdf:Description>
  <foaf:Project rdf:about="info:eu-repo/grantAgreement/EC/H2020/740859/">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">740859</dct:identifier>
    <dct:title>Advanced hoListic Adverse Drone Detection, Identification Neutralization</dct:title>
    <frapo:isAwardedBy>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </frapo:isAwardedBy>
  </foaf:Project>
</rdf:RDF>
70
14
views
downloads
Views 70
Downloads 14
Data volume 13.9 MB
Unique views 60
Unique downloads 13

Share

Cite as