000 01846naaaa2200349uu 4500
001 https://directory.doabooks.org/handle/20.500.12854/62761
005 20220220065057.0
020 _aKSP/1000051065
020 _a9783731504603
024 7 _a10.5445/KSP/1000051065
_cdoi
041 0 _aGerman
042 _adc
100 1 _aFischer, Yvonne
_4auth
245 1 0 _aWissensbasierte probabilistische Modellierung für die Situationsanalyse am Beispiel der maritimen Überwachung
260 _bKIT Scientific Publishing
_c2016
300 _a1 electronic resource (XVI, 217 p. p.)
506 0 _aOpen Access
_2star
_fUnrestricted online access
520 _aIn today’s surveillance systems, a multitude of sensors are used. Thus, the data volume is clearly increasing and the human decision maker has to be supported in analyzing this data in an intelligent way. This contribution deals with the process of situation assessment, which is analyzing real-time data with respect to pre-modeled situations of interest with a dynamic Bayesian network. The quality of the recognition is evaluated with a maritime dataset.
540 _aCreative Commons
_fhttps://creativecommons.org/licenses/by-sa/4.0/
_2cc
_4https://creativecommons.org/licenses/by-sa/4.0/
546 _aGerman
653 _adata fusion
653 _adynamic Bayesian networks
653 _aSituationsbewusstseinSituation assessment
653 _amaritime surveillance
653 _amaritime Überwachung
653 _aSituationsanalyse
653 _aDatenfusion
653 _asituation awareness
653 _adynamische Bayes’sche Netze
856 4 0 _awww.oapen.org
_uhttps://www.ksp.kit.edu/9783731504603
_70
_zDOAB: download the publication
856 4 0 _awww.oapen.org
_uhttps://directory.doabooks.org/handle/20.500.12854/62761
_70
_zDOAB: description of the publication
999 _c71623
_d71623