• DocumentCode
    1007931
  • Title

    Robust locally optimum detection of signals in dependent noise

  • Author

    Gerlach, Karl ; Sangston, Kevin J.

  • Author_Institution
    US Naval Res. Lab., Washington, DC, USA
  • Volume
    39
  • Issue
    3
  • fYear
    1993
  • fDate
    5/1/1993 12:00:00 AM
  • Firstpage
    1040
  • Lastpage
    1043
  • Abstract
    A robust locally optimum detector of a signal embedded in additive dependent nonGaussian noise is presented. The performance criterion is Bayes risk, the sample size is finite, and the uncertainty class of multivariate inputs is the ∈-contamination model. The locally optimum detector is shown to be a censored version of the nominal likelihood ratio
  • Keywords
    Bayes methods; noise; signal detection; ∈-contamination model; Bayes risk; additive dependent nonGaussian noise; multivariate inputs; nominal likelihood ratio; robust locally optimum detector; signal detection; uncertainty class; Additive noise; Costs; Density measurement; Detectors; Noise robustness; Particle measurements; Signal analysis; Signal detection; Size measurement; Testing; Uncertainty;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/18.256510
  • Filename
    256510
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1007931