1 Ocak 2013 Konferans bildirisi Açık Erişim

Kaplan, Gulay Buyukaksoy; Lana, Adnan

Dublin Core

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Kaplan, Gulay Buyukaksoy</dc:creator>
  <dc:creator>Lana, Adnan</dc:creator>
  <dc:date>2013-01-01</dc:date>
  <dc:description>In this study, the method we proposed GRNN alpha which is the specialized General Regression Neural Network (GRNN) algorithm, was used for estimating the target position in 3 Dimensional (3D) measurement environment. Although GRNN has only been used for estimating target velocity, GRNN alpha has the capability of estimating target's position as successful as the well known Kalman Filter (KF) algorithm. The performances of the mentioned algorithms are compared using simulated take-off and landing routes of aircrafts.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/91445</dc:identifier>
  <dc:identifier>oai:zenodo.org:91445</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Comparison of Proposed Target Tracking Algorithm, GRNN alpha, to Kalman Filter in 3D Environment</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>