| Paper: | SPTM-P2.6 | ||
| Session: | Detection | ||
| Time: | Tuesday, May 16, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, Classification Theory and Applications | ||
| Title: | Approaching Near Optimal Detection Performance via Stochastic Resonance | ||
| Authors: | Hao Chen, Pramod Varshney, Syracuse University, United States; James H. Michels, JHM Technologies, United States; Steven Kay, University of Rhode Island, United States | ||
| 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 and the probability of false alarm. Furthermore, the conditions required for potential performance improvement using SR are developed. Expression for the optimal stochastic resonance noise pdf which renders the maximum the probability of detection without the probability of false alarm 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. | ||
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