Stochastic Resonance Effect in Multisensor Decision and Fusion Systems
Yanxin Zhou, Enbin Song, Zhujun Cao, Weiyu Li, Tingting Wang · 2023
In this paper, stochastic resonance (SR) effect in multisensor decision and fusion systems is investigated under Bayes criterion. To this end, a necessary condition is established to determine the improvability of a detector using SR. Furthermore, we discuss performances of additive noises, which are added in different positions in the systems. The optimal noise in multisensor decision and fusion systems is proved to be constant. It is concluded that the benefits by adding additive noises only appears where noises are added after the sensor rules and before the fusion rule. Finally, a numerical example is presented to corroborate the above theoretical results.