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dc.contributor.authorSayeed, Akbar M.
Jones, Douglas L.
dc.creatorSayeed, Akbar M.
Jones, Douglas L. 2007-10-31T01:04:16Z 2007-10-31T01:04:16Z 1996-01-20 1996-01-20
dc.description Conference Paper
dc.description.abstract Time-frequency representations (TFRs) provide a powerful and flexible structure for designing optimal detectors in a variety of nonstationary scenarios. In this paper, we describe a TFR-based framework for optimal detection of arbitrary second-order stochastic signals, with certain unknown or random nuisance parameters, in the presence of Gaussian noise. The framework provides a useful model for many important applications including machine fault diagnostics and radar/sonar. We emphasize a subspace-based formulation of such TFR detectors which can be exploited in a variety of ways to design new techniques. In particular, we explore an extension based on multi-channel/sensor measurements that are often available in practice to facilitate improved signal processing. In addition to potentially improved performance, the subspace-based interpretation of such multi-channel detectors provides useful information about the physical mechanisms underlying the signals of interest.
dc.language.iso eng
dc.subjectsecond-order stochastic signals
Gaussian noise
multi-channel/sensor measurements
dc.subject.otherTime Frequency and Spectral Analysis
dc.title Time Frequency Detectors
dc.type Conference paper 2004-01-09
dc.citation.bibtexName inproceedings 2004-01-22
dc.contributor.orgDigital Signal Processing (
dc.subject.keywordsecond-order stochastic signals
Gaussian noise
multi-channel/sensor measurements
dc.citation.conferenceName Conference on Information Sciences and Systems
dc.type.dcmi Text
dc.type.dcmi Text

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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1225]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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