Blind Quadratic and Time Frequency Based Detectors from Training Data

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)en_US
dc.contributor.authorJones, Douglas L.en_US
dc.contributor.authorSayeed, Akbar M.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:48:18Z
dc.date.available2007-10-31T00:48:18Z
dc.date.issued1995-01-20en
dc.date.modified2004-01-22en_US
dc.date.note2004-01-09en_US
dc.date.submitted1995-01-20en_US
dc.descriptionConference paperen_US
dc.description.abstractTime-frequency based methods, particularly quadratic (Cohen's-class) representations, are often considered for detection in applications ranging from sonar to machine monitoring. We propose a method of obtaining near-optimal quadratic detectors directly from training data using Fisher's optimal linear discriminant to design a quadratic detector. This detector is optimal in terms of Fisher's scatter criterion as applied to the quadratic outer product of the data vector, and in early simulations appears to closely approximate the true optimal quadratic detector. By relating this quadratic detector to an equivalent operation on the Wigner distribution of a signal, we derive near-optimal time-frequency detectors. A simple example demonstrates the excellent performance of the method.en_US
dc.identifier.citationD. L. Jones and A. M. Sayeed, "Blind Quadratic and Time Frequency Based Detectors from Training Data," 1995.
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.1995.480410en_US
dc.identifier.urihttps://hdl.handle.net/1911/19984
dc.language.isoeng
dc.subjectquadratic representations*
dc.subjectFisher's optimal linear discriminant*
dc.subject.keywordquadratic representationsen_US
dc.subject.keywordFisher's optimal linear discriminanten_US
dc.subject.otherTime Frequency and Spectral Analysisen_US
dc.titleBlind Quadratic and Time Frequency Based Detectors from Training Dataen_US
dc.typeConference paper
dc.type.dcmiText
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