### 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 language | English (US) |
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Pages (from-to) | 695-698 |

Number of pages | 4 |

Journal | IEEE Signal Processing Letters |

Volume | 13 |

Issue number | 11 |

DOIs | |

State | Published - 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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## Cite this

Kay, S., Michels, J. H., Chen, H., & Varshney, P. K. (2006). Reducing probability of decision error using stochastic resonance.

*IEEE Signal Processing Letters*,*13*(11), 695-698. https://doi.org/10.1109/LSP.2006.879455