Defect Detection in Correlated Noise Dogandžić, Aleksandar Dogandžić, Aleksandar Eua-Anant, Nawanat
dc.contributor.department Electrical and Computer Engineering 2018-02-15T00:12:17.000 2020-06-30T02:03:17Z 2020-06-30T02:03:17Z Thu Jan 01 00:00:00 UTC 2004 2014-10-08 2004-01-01
dc.description.abstract <p>We present methods for detecting NDE defect signals in correlated noise having unknown covariance. The proposed detectors are derived using the statistical theory of generalized likelihood ratio (GLR) tests and multivariate analysis of variance (MANOVA). We consider both real and complex data models. To allow accurate estimation of the noise covariance, we incorporate secondary data containing only noise into detector design. Probability distributions of the GLR test statistics are derived under the null hypothesis, i.e. assuming that the signal is absent, and used for detector design. We apply the proposed methods to simulated and experimental data and demonstrate their superior performance compared with the detectors that neglect noise correlation.</p>
dc.description.comments <p>The following article appeared in <em>AIP Conference Proceedings</em> 700 (2004): 628 and may be found at doi:<a href="" target="_blank">10.1063/1.1711680</a>. </p>
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dc.identifier archive/
dc.identifier.articleid 1040
dc.identifier.contextkey 6217151
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ece_pubs/39
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 23:54:34 UTC 2022
dc.source.uri 10.1063/1.1711680
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.disciplines Engineering Physics
dc.subject.disciplines Multivariate Analysis
dc.subject.keywords Multivariate analysis
dc.subject.keywords nondestructive testing
dc.subject.keywords physics demonstrations
dc.subject.keywords probability theory
dc.subject.keywords statistical analysis
dc.title Defect Detection in Correlated Noise
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
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