The Application Of Stochastic Resonance In The Acoustic Signal Detection

In this dissertation, the application of stochastic resonance in the field of signal and information processing are researched.First, this paper reviews the research phylogeny of stochastic resonance, briefly summarizes the basic knowledge of nonlinear dynamics, the emphases of recent years research on stochastic resonance are briefly introduced too. When introduce the theory of nonlinear dynamics, emphasizes on the equation of Langevin, which is the basis of different algorism of stochastic resonance.Next, this paper detailed introduces the theory of stochastic resonance. Based on the theory, two different algorisms are introduced: Runge-Kutta algorism and the 2-ranked differential equation algorism. Also, the detailed deduction of this two algorisms are given in the paper.Next, this paper focuses on the different results arose by different system parameters when using the method of Stochastic Resonance to detect the weak periodic signal in heavily background hiss, and presents the method on how to adjust the system parameters. Detailed experiment results are given in the paper corresponding to the two different algorisms. Based on the experiment results, this paper compares the two different algorisms.The method of how to simulate the reverberation is introduced in the paper. After that, using this two algorisms in the simulated reverberation background signal detection. Then, give the detailed analysis of the results.In chapter 5, the two algorisms-- Runge-Kutta algorism and the 2-ranked differential equation algorism—are applied in the real reverberation gathered from the lake. Do the work: add sin signal in the reverberation, add squared signal in the reverberation, using the two algorisms to solve it, observing its output data. Based on the output data, evaluates the performance of the two algorisms.Finally, summarize the work of this dissertation and give the prospect of the future work.