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dc.contributor.authorSayeed, Akbar M.
Jones, Douglas L.
dc.creatorSayeed, Akbar M.
Jones, Douglas L.
dc.date.accessioned 2007-10-31T01:04:11Z
dc.date.available 2007-10-31T01:04:11Z
dc.date.issued 1996-01-20
dc.date.submitted 1996-01-20
dc.identifier.citation A. M. Sayeed and D. L. Jones, "Generalized Joint Signal Representations and Optimum Detection," 1996.
dc.identifier.urihttps://hdl.handle.net/1911/20329
dc.description Conference Paper
dc.description.abstract Generalized joint signal representations (JSRs) extend the scope of joint time-frequency representations (TFRs) to a richer class of nonstationary signals, but their use, just as in the case of TFRs, has been primarily limited to qualitative, exploratory data analysis. To exploit their potential more fully, JSR-based statistical signal processing techniques need to be developed that can be successfully applied in real-world problems. In this paper, we present an optimal detection framework based on arbitrary generalized quadratic JSRs, thereby making it applicable in a wide variety of detection scenarios involving nonstationary stochastic signals, noise and interference. For any given class of generalized JSRs, we characterize the corresponding class of detection scenarios for which such JSRs constitute canonical detectors, and derive the corresponding JSR-based detectors. Our formulation also yields a very useful subspace-based interpretation in terms of corresponding linear JSRs that we exploit to design optimal detectors based on only partial signal information.
dc.language.iso eng
dc.subjectJoint Signal Representations
Unitary Operator Covariance
Nonstationarity
Quadratic Detection
dc.subject.otherTime Frequency and Spectral Analysis
dc.title Generalized Joint Signal Representations and Optimum Detection
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.keywordJoint Signal Representations
Unitary Operator Covariance
Nonstationarity
Quadratic Detection
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.1996.543930


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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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