Konferans bildirisi Açık Erişim
Kaya, Engin; Kaplan, Gulay Buyukaksoy
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Kaplan, Gulay Buyukaksoy</subfield> <subfield code="u">TUBITAK BILGEM, Informat Technol Inst, TR-41400 Kocaeli, Turkey</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-tubitak-adresli-yayinlar</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="a">Creative Commons Attribution</subfield> <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1109/RadarConf2147009.2021.9455167</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Neural Network Based Drone Recognition Techniques With Non-Coherent S-Band Radar</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Kaya, Engin</subfield> <subfield code="u">TUBITAK BILGEM, Informat Technol Inst, TR-41400 Kocaeli, Turkey</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:aperta.ulakbim.gov.tr:240070</subfield> <subfield code="p">user-tubitak-adresli-yayinlar</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="2">opendefinition.org</subfield> <subfield code="a">cc-by</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2021-01-01</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/240070/files/bib-955f9093-cecc-492b-950a-b10bb016c1a5.txt</subfield> <subfield code="z">md5:a9d6f3866c08bd9e445097adbd93944d</subfield> <subfield code="s">171</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <controlfield tag="005">20221007104535.0</controlfield> <controlfield tag="001">240070</controlfield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">conferencepaper</subfield> </datafield> <datafield tag="711" ind1=" " ind2=" "> <subfield code="a">2021 IEEE RADAR CONFERENCE (RADARCONF21): RADAR ON THE MOVE</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a">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.</subfield> </datafield> </record>
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