Title :
Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors
Author :
Chen, Hao ; Varshney, Pramod K. ; Kay, Steven M. ; Michels, James H.
Author_Institution :
Syracuse Univ., Syracuse
fDate :
7/1/2007 12:00:00 AM
Abstract :
This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in binary hypothesis testing problems. The mechanism for SR noise enhanced signal detection is explored. The detection performance of a noise modified detector is derived in terms of the probability of detection PD and the probability of false alarm PFA. Furthermore, sufficient conditions are established to determine the improvability of a fixed detector using SR. The form of the optimal noise pdf is determined and the optimal stochastic resonance noise pdf which renders the maximum PD without increasing PFA is derived. Finally, an illustrative example is presented where performance comparisons are made between detectors where the optimal stochastic resonance noise, as well as Gaussian, uniform, and optimal symmetric noises are applied to enhance detection performance.
Keywords :
Gaussian noise; signal detection; binary hypothesis testing problems; fixed detectors; noise modified detector; nonGaussian noise; optimal noise pdf; signal detection; stochastic resonance effect; Detectors; Gaussian noise; Noise level; Noise robustness; Nonlinear systems; Signal analysis; Signal detection; Signal to noise ratio; Stochastic resonance; Strontium; Hypothesis testing; non-Gaussian noise; nonlinear systems; signal detection; stochastic resonance (SR);
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2007.893757