Reducing Probability of Decision Error Using Stochastic Resonance

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...

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Bibliographic Details
Published in:IEEE signal processing letters 2006-11, Vol.13 (11), p.695-698
Main Authors: Kay, S., Michels, J.H., Hao Chen, Varshney, P.K.
Format: Article
Language:English
Subjects:
Approximation
Decision making
Delta function
Detectors
Errors
Modeling
Optimization
Pattern classification
Probability density function
Probability density functions
Random variables
Receivers
Signal detection
Statistical analysis
Stochastic resonance
Testing
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Summary: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
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2006.879455