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dc.contributor.authorSayeed, Akbar M.
dc.creatorSayeed, Akbar M.
dc.date.accessioned 2007-10-31T01:04:23Z
dc.date.available 2007-10-31T01:04:23Z
dc.date.issued 1997-01-20
dc.date.submitted 1997-01-20
dc.identifier.citation A. M. Sayeed, "Data Driven Signal Detection and Classification," 1997.
dc.identifier.urihttps://hdl.handle.net/1911/20334
dc.description Conference Paper
dc.description.abstract In many practical detection and classification problems, the signals of interest exhibit some uncertain nuisance parameters, such as the unknown delay and Doppler in radar. For optimal performance, the form of such parameters must be known and exploited as is done in the generalized likelihood ratio test (GLRT). In practice, the statistics required for designing the GLRT processors are not available a priori and must be estimated from limited training data. Such design is virtually impossible in general due to two major difficulties: identifying the appropriate nuisance parameters, and estimating the corresponding GLRT statistics. We address this problem by using recent results that relate joint signal representations (JSRs), such as time-frequency and time-scale representations, to quadratic GLRT processors for a wide variety of nuisance parameters. We propose a general data-driven framework that: 1) identifies the appropriate nuisance parameters from an arbitrarily chosen finite set, and 2) estimates the second-order statistics that characterize the corresponding JSR-based GLRT processors.
dc.language.iso eng
dc.subjectgeneralized likelihood ratio test
joint signal representations
dc.subject.otherTime Frequency and Spectral Analysis
dc.title Data Driven Signal Detection and Classification
dc.type Conference paper
dc.date.note 2004-01-09
dc.citation.bibtexName inproceedings
dc.date.modified 2004-01-22
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordgeneralized likelihood ratio test
joint signal representations
dc.citation.conferenceName IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.1997.604670


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1278]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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