Reducing probability of decision error using stochastic resonance

Authors

Steven Kay, University of Rhode Island
James H. Michels, JHM Technologies
Hao Chen, Syracuse University
Pramod K. Varshney, Syracuse University

Document Type

Article

Date of Original Version

11-1-2006

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. © 2006 IEEE.

Publication Title, e.g., Journal

IEEE Signal Processing Letters

Volume

13

Issue

11

Citation/Publisher Attribution

Kay, Steven, James H. Michels, Hao Chen, and Pramod K. Varshney. "Reducing probability of decision error using stochastic resonance." IEEE Signal Processing Letters 13, 11 (2006): 695-698. doi: 10.1109/LSP.2006.879455.