Study On Application Of Stochastic Resonance Theory On Faint Signal Detection

Stochastic resonance (SR) is different from traditional linear methods (such as correlation, equality superposition, filter etc) in signal detection. SR can not only wipe off noise but can also turn parts of noise energy into signal energy to increase the signal-to-noise rate (SNR). Focusing on faint signal detection, this thesis presents the principle and practicable method to utilize SR for faint signal detection. The thesis consists of three parts: 1) research on the mechanism of faint signal detection based on SR, 2) research on adaptive SR; 3) study of the application of SR on the fault diagnose for electronic device.In the first part of the thesis, according to signal detection requirement, we studied the relationship among bistable SR system, signal, noise based on colligating previous study results about SR by other researchers.There are strict conditions for stochastic resonance among system, signal and noise. These conditions are hardly being satisfied in signal measurement. In the second part of the thesis, considering actual situation of faint signal detection, an adaptive SR systems based on LRS and 'anticipate parameter'is originally set up and applied on the fault diagnose of electronic circuits. The feasibility has been verified. Contrasting to previous SR system, which can only measure one signal frequency, our new system realized multi-signal detection to make the SR theory for signal detection practicable.In the third part of the thesis, taking switch mode power supply as detecting object, a novel non-contact method based on SR theory to utilize electromagnetic radiation for the fault diagnose of electronic device is studied. It is a thoroughly new method and has not been previously reported in circuit fault diagnose.Finally, based on H-H model of neuron, we simulated the SR to help neuron increasing tactile sensitivity when the stimulating signal is under the threshold via an algorithm to solve nonlinear the H-H equation. The simulation results are promising while the biological experiment is still under going. The study explores the further application of SR technology on biomedical engineering.