Classification of target primitives with sonar using two non-parametric data fusion methods

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buir.advisorBarshan, Billur
dc.contributor.authorAyrulu, Birsel
dc.date.accessioned2016-01-08T20:13:50Z
dc.date.available2016-01-08T20:13:50Z
dc.date.issued1996
dc.descriptionAnkara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1996.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 1996.en_US
dc.descriptionIncludes bibliographical references leaves 233-236.en_US
dc.description.abstractIn this study, physical models are used to model reflections from target primitives commordy encountered in a mobile robot’s environment. These tcirgets are differentiated by employing a multi-transducer pulse/echo system which relies on both cimplitude and time-of-flight (TOP) data in the feature fusion process, cillowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused for multiple logical soncirs at different geographical sites. Feature data from multiple logical sensors are fused with Dernpster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Using three sensing nodes, improvement in differentiation is 20% without false decision, however, at the cost of additional computation. Simulation results are verified by experiments with real sonar systems. As an alternative method, neural networks are used for incorporating lecirning of identifying pcirameter relations of target primitives. Amplitude and time-of-flight measurements of .‘]1 sensor pairs cire fused with these neural networks. Improvement in differentiation is 72% with 28% false decision at the cost of elapsed time until the network learns these patterns. These two approaches help to overcome the vulnerability of echo amplitude to noise and enable the modeling of non-parametric uncertainty.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T20:13:50Z (GMT). No. of bitstreams: 1 1.pdf: 78510 bytes, checksum: d85492f20c2362aa2bcf4aad49380397 (MD5)en
dc.description.statementofresponsibilityAyrulu, Birselen_US
dc.format.extentxxiii, 236 leavesen_US
dc.identifier.urihttp://hdl.handle.net/11693/17832
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectultrasonic transducersen_US
dc.subjectsonaren_US
dc.subjecttarget classificationen_US
dc.subjectsensor-hased roboticsen_US
dc.subjectmulti-sensor data fusion and integrationen_US
dc.subjectnon-parametric data fusionen_US
dc.subjectevidential reasoningen_US
dc.subjectbelief functionsen_US
dc.subjectDempster-Shafer rmle of combinationen_US
dc.subjectlogical sensingen_US
dc.subjectartificial neural networks.en_US
dc.subject.lccTJ211 .A97 1996en_US
dc.subject.lcshRobotics.en_US
dc.subject.lcshArtificial intelligence.en_US
dc.subject.lcshMultisensor data fusion.en_US
dc.titleClassification of target primitives with sonar using two non-parametric data fusion methodsen_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical and Electronic Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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