Performance of the locally optimum detector in a correlated non-Gaussian disturbance

dc.contributor.authorChakravarthi P.R.
dc.contributor.authorWeiner D.D.
dc.contributor.authorOzturk A.
dc.date.accessioned2019-10-27T00:34:37Z
dc.date.available2019-10-27T00:34:37Z
dc.date.issued1992
dc.departmentEge Üniversitesien_US
dc.description35th Midwest Symposium on Circuits and Systems, MWSCAS 1992 -- 9 August 1992 through 12 August 1992 -- 146292en_US
dc.description.abstractIn radar problems involving weak signal detection conventional space-time processing cannot be used to separate the target from the clutter when the spatial and Doppler spectra of the target and clutter overlap. In such problems the concept of the locally optimum detector (LOD) [1] is useful in coming up with a decision rule to discriminate between the two hypotheses of signal present or signal absent. The clutter which may be correlated can arise from either a non-Gaussian or Gaussian random process. For correlated multivariate student-T distributed clutter it is shown in this paper that significant performance improvements can be obtained with a LOD as opposed to the conventional Gaussian linear receiver. © 1992 IEEE.en_US
dc.identifier.doi10.1109/MWSCAS.1992.271098
dc.identifier.endpage1319en_US
dc.identifier.isbn780305108
dc.identifier.issn1548-3746
dc.identifier.issn1548-3746en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1316en_US
dc.identifier.urihttps://doi.org/10.1109/MWSCAS.1992.271098
dc.identifier.urihttps://hdl.handle.net/11454/24276
dc.identifier.volume1992-Augusten_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofMidwest Symposium on Circuits and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titlePerformance of the locally optimum detector in a correlated non-Gaussian disturbanceen_US
dc.typeConference Objecten_US

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