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dc.contributor.authorHuber, Marco*
dc.date.accessioned2021-02-11T23:54:57Z
dc.date.available2021-02-11T23:54:57Z
dc.date.issued2009*
dc.date.submitted2019-07-30 19:59:17*
dc.identifier34253*
dc.identifier.issn18673813*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/57004
dc.description.abstractA probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions.*
dc.languageEnglish*
dc.relation.ispartofseriesKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Universität Karlsruhe, Intelligent Sensor-Actuator-Systems Laboratory*
dc.subjectQA75.5-76.95*
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer scienceen_US
dc.subject.otherBayesian estimation*
dc.subject.otherdecision theory*
dc.subject.othersensor management*
dc.subject.otherinformation theory*
dc.subject.otherGaussian mixtures*
dc.titleProbabilistic Framework for Sensor Management*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000012224*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783866444058*
oapen.pagesVI, 159 p.*
oapen.volume7*
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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