DocumentCode :
455035
Title :
Approaching Near Optimal Detection Performance via Stochastic Resonance
Author :
Chen, Hao ; Varshney, Pramod K. ; Michels, James H. ; Kay, Steven
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., NY
Volume :
3
fYear :
2006
fDate :
14-19 May 2006
Abstract :
This paper considers the stochastic resonance (SR) effect in the two hypotheses signal detection problem. Performance of a SR enhanced detector is derived in terms of the probability of detection PD and the probability of false alarm PFA. Furthermore, the conditions required for potential performance improvement using SR are developed. Expression for the optimal stochastic resonance noise pdf which renders the maximum po without increasing PFA is derived. By further strengthening the conditions, this approach yields the constant false alarm rate (CFAR) receiver. Finally, detector performance comparisons are made between the optimal SR noise, Gaussian, uniform and optimal symmetric pdf noises
Keywords :
probability; signal detection; stochastic processes; constant false alarm rate; detection probability; hypotheses signal detection problem; near optimal detection performance; stochastic resonance; Detectors; Gaussian noise; Noise level; Nonlinear systems; Probability; Signal detection; Signal to noise ratio; Stochastic resonance; Strontium; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
ISSN :
1520-6149
Print_ISBN :
1-4244-0469-X
Type :
conf
DOI :
10.1109/ICASSP.2006.1660645
Filename :
1660645
Link To Document :
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