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dc.contributor.authorBarshan, B.en_US
dc.contributor.authorEravci, B.en_US
dc.date.accessioned2016-02-08T09:44:20Z
dc.date.available2016-02-08T09:44:20Z
dc.date.issued2011-10-04en_US
dc.identifier.issn0018-9251
dc.identifier.urihttp://hdl.handle.net/11693/21291
dc.description.abstractWe propose a novel and robust algorithm for antenna scan type (AST) recognition in electronic warfare (EW). The stages of the algorithm are scan period estimation, preprocessing (normalization, resampling, averaging), feature extraction, and classification. Naive Bayes (NB), decision-tree (DT), artificial neural network (ANN), and support vector machine (SVM) classifiers are used to classify five different ASTs in simulation and real experiments. Classifiers are compared based on their accuracy, noise robustness, and computational complexity. DT classifiers are found to outperform the others.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Aerospace and Electronic Systemsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TAES.2012.6324669en_US
dc.subjectNaive bayesen_US
dc.subjectNoise robustnessen_US
dc.subjectPeriod estimationen_US
dc.subjectResamplingen_US
dc.subjectRobust algorithmen_US
dc.subjectAlgorithmsen_US
dc.subjectElectronic warfareen_US
dc.subjectFeature extractionen_US
dc.subjectNeural networksen_US
dc.subjectRadar antennasen_US
dc.subjectSupport vector machinesen_US
dc.titleAutomatic radar antenna scan type recognition in electronic warfareen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage2908en_US
dc.citation.epage2931en_US
dc.citation.volumeNumber48en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1109/TAES.2012.6324669en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US


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