Abstract
We study the design enhancement of the bistable stochastic resonance (SR) performance on sinusoidal signal and Gaussian white noise. The bistable system is known to show an SR property; however the performance improvement is limited. Our work presents two main contributions: first, we proposed a parallel array bistable system with independent components and averaged output; second, we give a deduction of the output signal-to-noise ratio (SNR) for this system to show the performance. Our examples show the enhancement of the system and how different parameters influence the performance of the proposed parallel array.
Highlights
Stochastic resonance has attracted considerable attention over the past decades
We provide examples to illustrate the properties of our proposed bistable parallel array system
Though the two systems are set by different parameters, they both display evolutions of the resulting output signal-to-noise ratio (SNR) of (8), as a function of the noise variance, in some typical conditions
Summary
Stochastic resonance has attracted considerable attention over the past decades. SR is defined as a phenomenon that is manifest in nonlinear systems whereby generally feeble input information (such as a weak signal) can be amplified and optimized by the assistance of noise. Even with the uncoupled components, the performance of the system still has room to be improved Since, in these types of array, the components are not independent of each other, the independence in statistics is an importance feature to the best performance. Mathematical Problems in Engineering sensors, and focuses on the output SNR performance. This is different from traditional parallel SR system since traditional system uses one receiving sensor and parallel array processing components so that input for each component is not independent in statistics. To analyse the performance of this array theoretically, we give a complete proof on output SNR and experiments to demonstrate the parameter influences. E(⋅) stands for ensemble average, upper dot ȧ denotes a time derivative of a, A(b) represents the derivative of A with respect to b, δ(⋅) is Dirac delta function, and f ∗ g represents the convolution of f and g
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