Study of the Method of Multi-Frequency Signal Detection Based on the Adaptive Stochastic Resonance

and Applied Analysis 3 and finally finds and extracts the frequency of unknownweak cycle signal in the frequency domain. 3. Adaptive Stochastic Resonance Detection for Low-Frequency Signals 3.1. Measurement Index and Iterative Algorithm. Adaptive stochastic resonance signal detection involves two important factors: measurement index and iterative algorithm. (1) Measurement Index. Selecting the appropriate measurement index to measure the effectiveness of the system output which means whether to generate stochastic resonance. The commonly measurement index in the study of stochastic resonance contains signal-to-noise ratio (SNR), autocorrelation function, cross-correlation function, mutual information, residence time distribution, [20–23] and so on. For the detection of low-frequency signals, this paper ismainly based on the SNR to extract effective signal. SNR is an index of the proportion that the energy of input signal frequency f 0 is contained in the system output signal y(t) = g(x(t)), which is defined as SNR = 10 log S N = 10 log S (f 0 )