
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
  <dc:contributor>Stanković, Zoran Ž. 1968-</dc:contributor>
  <dc:contributor>Marković, Vera</dc:contributor>
  <dc:contributor>Dončov, Nebojša</dc:contributor>
  <dc:contributor>Maleš-Ilić, Nataša</dc:contributor>
  <dc:contributor>Drajić, Dejan</dc:contributor>
  <dc:date>2020</dc:date>
  <dc:language>srp</dc:language>
  <dc:format>133 lista</dc:format>
  <dc:format>8456412 bytes</dc:format>
  <dc:title xml:lang="srp">Nadgledanje ciljeva iza linije horizonta integracijom podataka sa OTN radara i drugih mornaričkih senzora</dc:title>
  <dc:identifier>https://phaidrani.ni.ac.rs/o:1709</dc:identifier>
  <dc:identifier>cobiss:30842377</dc:identifier>
  <dc:identifier>thesis:8181</dc:identifier>
  <dc:creator>Nikolić, Dejan</dc:creator>
  <dc:rights>http://creativecommons.org/licenses/by-nc-sa/2.0/at/legalcode</dc:rights>
  <dc:type>info:eu-repo/semantics/bachelorThesis</dc:type>
  <dc:description xml:lang="eng">The subject of this dissertation’s research is the integration of data
obtained from sensors that monitor the sea surface beyond the line of
horizon. The primary sensor network is composed of OTH (Over the
Horizon) radars, while the secondary data source is the AIS system
(Automated Information System), both satellite and terrestrial. With
the integration of all collected data, a unique operational picture is
formed in order to control maritime traffic on the high seas. The
emphasis is on the control of the Exclusive Economic Zone in order
to increase the safety of navigation, preserve natural resources and
prevent illegal activities. In order to solve the integration problem in
an efficient way, two new algorithms for data integration have been
proposed and implemented. The dissertation first presents an
algorithm for efficient data fusion within network consisting of 2 or
more OTH radars. Then, an algorithm for efficient integration of OTH
radar tracks and other naval sensors, primarily AIS systems, was
presented. For both algorithms, the results obtained through
exploitation in real working environment (the Gulf of Guinea) are
presented. At the end of this dissertation, the architecture of a hybrid
empirical-neural model is proposed to estimate the number of false
targets caused by the strong atmospheric disturbances, including even
meteo-tsunamis, and to reduce false alarms in the OTH sensor
network. The architecture of this model is based on the PNN
(Probabilistic Neural Network).</dc:description>
  <dc:description xml:lang="srp">Biografija autora: list 130,Bibliografija: 120-125.  Datum odbrane: 25.12.2020. Radiolocation</dc:description>
</oai_dc:dc>
