阈值阵列模型下的超阈值随机共振信噪比增益

Signal-to-Noise Ratio Gain of Suprathreshold Stochastic Resonance Based on Threshold Array Model

研究了阈值阵列模型和超阈值随机共振现象。对该模型进行剖析,认为阈值阵列系统可以分解为单个阈值系统与集总平均器的级联。为了研究周期输入下的超阈值随机共振现象,理论分析了周期输入下的阈值阵列模型输出随机过程的统计特性,以输出信噪比增益作为随机共振的测度,固定输入信噪比,观测输出信噪比增益相对于阈值噪声方差的变化规律。证实当输入噪声为高斯噪声时,在阈值阵列系统中加入统计独立的高斯白噪声可使输出信噪比增益大于1,当输入噪声为非高斯噪声时,可获得更高的输出信噪比增益。

The threshold-array model is presented to study suprathreshold stochastic resonance （SSR） phenomenon. The analysis of the presented model proves that the threshold-array sys-tem can be decomposed into a cascade of a single threshold system and an ensemble averager. In order to study the SSR with periodic input, the statistical properties of the output process of the threshold-array model are evaluated. With a fixed input signal-to-noise ratio（SNR）, the output SNR gain of the model varies in a non-monotonic way when injecting independent threshold noises into the array. If the input noise is Gaussian, when adding independent Gauss-Jan white threshold noises into the array, a SNR gain larger than unity can be obtained. More-over, when the input noise is non-Gaussian, there will be a better SNR gain.