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Application of Stochastic Resonance for Detecting Weak Periodic Signals

Yongxiang Zhang

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Abstract

Based on the stochastic resonance(SR) theory,taking a nonlinear bi-stable system as research object,the method of weak signal detection based on SR was discussed for the weak singles need to be detected in a strong background noise.This method was applied to detect the weak frequency component signals in the strong noise background.The theoretical analysis and numerical simulation indicated that this method is simple,robust and reliable.The result showed that the weak sinusoid signal of lower signal-to-noise ratio can be effectively extracted from heavy noise when the data length is short.

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What this paper is about

Based on the stochastic resonance(SR) theory,taking a nonlinear bi-stable system as research object,the method of weak signal detection based on SR was discussed for the weak singles need to be detected in a strong background noise.This method was applied to detect the weak frequency component signals in the strong noise background.The theoretical analysis and numerical simulation indicated that this method is simple,robust and reliable.The result showed that the weak sinusoid signal of lower signal-to-noise ratio can be effectively extracted from heavy noise when the data length is short.

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Available abstract

Based on the stochastic resonance(SR) theory,taking a nonlinear bi-stable system as research object,the method of weak signal detection based on SR was discussed for the weak singles need to be detected in a strong background noise.This method was applied to detect the weak frequency component signals in the strong noise background.The theoretical analysis and numerical simulation indicated that this method is simple,robust and reliable.The result showed that the weak sinusoid signal of lower signal-to-noise ratio can be effectively extracted from heavy noise when the data length is short.

Key concepts: Stochastic resonance, Noise (video), SIGNAL (programming language), Nonlinear system, Mathematics, Component (thermodynamics), Statistical physics, Detection theory

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