Reducing probability of decision error using stochastic resonance

Steven Kay, James H. Michels, Hao Chen, Pramod K. Varshney

Research output: Contribution to journalArticle

53 Scopus citations

Abstract

The problem of reducing the probability of decision error of an existing binary receiver that is suboptimal using the ideas of stochastic resonance is solved. The optimal probability density function of the random variable that should be added to the input is found to be a Dirac delta function, and hence, the optimal random variable is a constant. The constant to be added depends upon the decision regions and the probability density functions under the two hypotheses and is illustrated with an example. Also, an approximate procedure for the constant determination is derived for the mean-shifted binary hypothesis testing problem.

Original languageEnglish (US)
Pages (from-to)695-698
Number of pages4
JournalIEEE Signal Processing Letters
Volume13
Issue number11
DOIs
StatePublished - Nov 1 2006

Keywords

  • Modeling
  • Pattern classification
  • Signal detection

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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