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dc.contributor.authorAyrulu, Birselen_US
dc.contributor.authorBarshan, Billuren_US
dc.coverage.spatialBaden-Baden, Germany,
dc.date.accessioned2016-02-08T11:57:19Z
dc.date.available2016-02-08T11:57:19Z
dc.date.issued2001-08en_US
dc.identifier.urihttp://hdl.handle.net/11693/27592
dc.descriptionDate of Conference: 20-22 Aug. 2001
dc.descriptionConference name: Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001
dc.description.abstractThe comparison of different classification and fusion techniques was done for target classification and localization with sonar. Target localization performance of artificial neural networks (ANN) was found to be better than the target differentiation algorithm (TDA) and fusion techniques. The target classification performance of non-parametric approaches was better than that of parameterized density estimator (PDE) using homoscedastic and heteroscedastic NM for statistical pattern recognition techniques.en_US
dc.language.isoEnglishen_US
dc.source.titleConference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001en_US
dc.relation.isversionofhttps://doi.org/10.1109/MFI.2001.1013503
dc.subjectArtificial neural networksen_US
dc.subjectEvidential reasoningen_US
dc.subjectLocalizationen_US
dc.subjectSensor data fusionen_US
dc.subjectSonaren_US
dc.subjectStatistical pattern recognitionen_US
dc.subjectTarget classificationen_US
dc.subjectVotingen_US
dc.subjectAlgorithmsen_US
dc.subjectClassification (of information)en_US
dc.subjectNeural networksen_US
dc.subjectPattern recognitionen_US
dc.subjectUltrasonic transducersen_US
dc.subjectIntelligent systemsen_US
dc.subjectSonaren_US
dc.titleComparative analysis of different approaches to target classification and localization with sonaren_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage25en_US
dc.citation.epage30en_US
dc.identifier.doi10.1109/MFI.2001.1013503
dc.publisherIEEE


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