TY - JOUR
T1 - Improving sequential detection performance via stochastic resonance
AU - Varshney, Pramod K.
AU - Michels, James H.
AU - Chen, Hao
PY - 2008
Y1 - 2008
N2 - In this letter, we present a novel instance of the stochastic resonance effect in sequential detection. For a general binary hypotheses sequential detection problem, the detection performance is evaluated in terms of the expected sample size under both hypotheses. Improvability conditions are established for an injected noise to reduce at least one of the expected sample sizes for a sequential detection system using stochastic resonance. The optimal noise is also determined under such criteria. An illustrative example is presented where performance comparisons are made between the original detector and different noise modified detectors.
AB - In this letter, we present a novel instance of the stochastic resonance effect in sequential detection. For a general binary hypotheses sequential detection problem, the detection performance is evaluated in terms of the expected sample size under both hypotheses. Improvability conditions are established for an injected noise to reduce at least one of the expected sample sizes for a sequential detection system using stochastic resonance. The optimal noise is also determined under such criteria. An illustrative example is presented where performance comparisons are made between the original detector and different noise modified detectors.
KW - Hypothesis testing
KW - Nonlinear systems
KW - Sequential detection
KW - Sequential probability ratio test
KW - Stochastic resonance
UR - http://www.scopus.com/inward/record.url?scp=67650134502&partnerID=8YFLogxK
U2 - 10.1109/LSP.2008.2001980
DO - 10.1109/LSP.2008.2001980
M3 - Article
AN - SCOPUS:67650134502
SN - 1070-9908
VL - 15
SP - 685
EP - 688
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
ER -