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      Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework

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      Author
      Bayram, S.
      Gezici, Sinan
      Date
      2012-02-20
      Source Title
      Digital Signal Processing: A Review Journal
      Print ISSN
      1051-2004
      Publisher
      Elsevier
      Volume
      22
      Issue
      3
      Pages
      391 - 406
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman-Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results.
      Keywords
      Binary hypothesis-testing
      Composite hypothesis-testing
      Least-favorable prior
      Neyman-Pearson
      Stochastic resonance (SR)
      Binary composites
      Binary hypothesis-testing
      Detection performance
      Detection probabilities
      Independent noise
      Least-favorable prior
      Maximum probability
      Neyman-Pearson
      Parameter values
      Statistical characterization
      Stochastic resonances
      Sufficient conditions
      Theoretical result
      Additive noise
      Detectors
      Magnetic resonance
      Optimization
      Probability distributions
      Circuit resonance
      Permalink
      http://hdl.handle.net/11693/21491
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.dsp.2012.02.003
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      • Department of Electrical and Electronics Engineering 3524
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