Research And Application Of Stochastic Resonance On Signal Detection

Stochastic resonance theory as a branch of nonlinear science emerged nearly thirty years, the theory is still in continuous improvement and development process, while also expanding its application areas. This article focuses on two nonlinear models of stochastic resonance and their signal detection properties.The second chapter describes the stochastic resonance theory based on adiabatic elimination theory and linear response theory. The two main methods of the analytical approximation are introduced. So far no research results have been reported on the analytical solution and approximate solution about the nonlinear models. Therefore, the nonlinear models are studied further using the Runge-Kutta method.The rationality of the definition of SNR and SNR gain is demonstrated.The third chapter focuses on SNR gain of the nonlinear systems. The systems are driven by the periodic sinusoidal signal and periodic rectangular pulse respectively, and studied by using the Runge-Kutta method. We find that the SNR gain exhibits the stochastic resonance behavior, and exceeds unity on some occasions. These results are the latest developments of the nonl inear systems and make the signal detection based on these the nonlinear models possible.The fourth chapter analyzes the system parameters of nonlinear models on the impact of stochastic resonance to facilitate the practical application by selecting the appropriate system parameters. The scope of the input signal frequency is analyzed and is found that the input signal frequency is only applicable to low-frequency region. To expand the scope of the input signal frequency, we use the twice sampling method and obtain better results. Whit the increase of the series of stochastic resonance systems, the SNR can be improved. Detection of bearing fault signals by using stochastic resonance obtains better test results.The fifth chapter combines the characteristics between chaos and stochastic resonance in signal detection and proposed the chaotic oscillator method of weak signal detection based on stochastic resonance. Integration of two nonlinear methods for signal detection is a useful new attempt.The sixth chapter investigates the nonlinear systems while they are driven by the aperiodic signal and single pulse signal respectively. This paper proposes a measure of aperiodic stochastic resonance-the gain of the cross correlation coefficient. When the nonlinear systems are driven by the aperiodic signal, their gains of the cross correlation coefficient exhibit the stochastic resonance behavior, and exceed unity. When the nonlinear systems are driven by the single pulse signal, the monostable systems can extract the single pulse signal waveform from the background noise.