Theory and Application of Weak Signal Detection Based on Stochastic Resonance Mechanism

Lin Cui,Lunwen Wang,Junan Yang,Hui Liu,Chi-Hua Chen

doi:10.1155/2021/5553490

Lin Cui, Lunwen Wang + Show 3 more

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https://doi.org/10.1155/2021/5553490

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Abstract

Stochastic resonance is a new type of weak signal detection method. Compared with traditional noise suppression technology, stochastic resonance uses noise to enhance weak signal information, and there is a mechanism for the transfer of noise energy to signal energy. The purpose of this paper is to study the theory and application of weak signal detection based on stochastic resonance mechanism. This paper studies the stochastic resonance characteristics of the bistable circuit and conducts an experimental simulation of its circuit in the Multisim simulation environment. It is verified that the bistable circuit can achieve the stochastic resonance function very well, and it provides strong support for the actual production of the bistable circuit. This paper studies the stochastic resonance phenomenon of FHN neuron model and bistable model, analyzes the response of periodic signals and nonperiodic signals, verifies the effect of noise on stochastic resonance, and lays the foundation for subsequent experiments. It proposes to feedback the link and introduces a two-layer FHN neural network model to improve the weak signal detection performance under a variable noise background. The paper also proposes a multifault detection method based on the total empirical mode decomposition of sensitive intrinsic mode components with variable scale adaptive stochastic resonance. Using the weighted kurtosis index as the measurement index of the system output can not only maintain the similarity between the system output signal and the original signal but also be sensitive to impact characteristics, overcoming the missed or false detection of the traditional kurtosis index. Experimental research shows that this method has better noise suppression ability and a clear reproduction effect on details. Especially for images contaminated by strong noise (D = 500), compared with traditional restoration methods, it has better performance in subjective visual effects and signal-to-noise ratio evaluation.

Highlights

Stochastic resonance is a new type of weak signal detection method
Compared with traditional noise suppression technology, stochastic resonance uses noise to enhance weak signal information, and there is a mechanism for the transfer of noise energy to signal energy. e purpose of this paper is to study the theory and application of weak signal detection based on stochastic resonance mechanism. is paper studies the stochastic resonance characteristics of the bistable circuit and conducts an experimental simulation of its circuit in the Multisim simulation environment
Compared with the traditional method, the difference is that stochastic resonance uses noise to enhance useful signals in a nonlinear system and realizes weak signal enhancement detection, so this detection method can well retain the details of the signal, and there is no damage to the characteristic signal

Summary

Measures of Stochastic Resonance

Rough the above analysis, we know that, in the process of detecting weak stochastic resonance signals and adjusting the system parameters a and b to make the system output tend to be the best output, the entropy value of the system output signal will tend to the entropy value of the original pure signal At this time, the output signal-to-noise ratio of the system will reach the maximum, and the amplitude of the detected weak signal will reach the maximum; that is, the conversion of noise energy to the frequency of the periodic driving force reaches the maximum. We still need to point out that, for systems with high noise intensity, there will be a large proportion of the interference amplitude with a large value, and their effect is likely to make the moving point occur and directly We call this type of interference “overinterference”; this type of overinterference can lead to the phenomenon of “over-stochastic resonance.”. E smallest system module of the single-chip microcomputer provides an executable program platform for the singlechip microcomputer. e single-chip microcomputer generates pseudorandom numbers that obey the Gaussian distribution; the D/A conversion module converts the pseudorandom numbers into Gaussian noise voltages with fixed variance; we need to process the waveform adjustment module. e DC quantity and amplitude are adjusted and output

Experimental Method

Conclusions