Speedier sequential tests via stochastic resonance

Stochastic resonance (SR) is a phenomenon long investigated by physicists that has recently attracted some interest in the signal processing literature. In this paper, we explore the potential benefits of the SR effect for shift-in-mean detection problems, specifically focusing on sequential decision rules. Amenable formulas for the optimal distribution of the SR noise, as well as an asymptotic comparison with the traditional Neyman-Pearson approach are obtained.

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