Title
Reducing probability of decision error using stochastic resonance
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.