Variable step size monostable stochastic resonance weak signal detection method

Abstract

The invention discloses a variable step size monostable stochastic resonance weak signal detection method. According to the variable step size monostable stochastic resonance weak signal detection method, by means of combination of characteristics of a monostable system and fundamental principles of a fourth order Runge-Kutta method, step size can be adjusted and computed and direct detection of weak large-signals and weak small-signals can be achieved. Compared with an existing method in which a bistable system with two structure parameters is utilized for detecting weak signals, the variable step size monostable stochastic resonance weak signal detection method does not need to lead the weak large-signals to be subjected into shift of frequency spectrum or redesign of a system of different structure parameters, can directly achieve weak signal detection of different frequency characteristics through one stochastic resonance system, thereby being easy in parameter adjustment and greatly saving hardware costs.

Description

technical field [0001] The invention belongs to a method for detecting weak characteristic signals under low signal-to-noise ratio, in particular to a method for detecting weak periodic signals by using monostable stochastic resonance. Background technique [0002] Stochastic Resonance (SR) is a theory proposed by Benzi and his partners in the 1980s to explain the periodic changes of Earth's glaciers. Once the theory is put forward, it is widely used in various fields, such as: biomedicine, image processing, geological exploration and so on. In the field of signal detection, stochastic resonance also has important applications. The signal detection theory based on stochastic resonance is different from traditional signal detection methods. Traditional signal detection methods mostly suppress noise to make the signal stand out. While suppressing noise, the signal will also be weakened. The stochastic resonance theory points out that when the signal and noise are not In a li...

AI Technical Summary

Problems solved by technology

However, based on the stochastic resonance theory of modulation, it is necessary to move the high-frequency signal to the low frequency through modulation first, and then send the low-frequency signal into the stochastic resonance system; and appropriately reduce the system structure parameters to achieve stochastic resonance, but excessively reduce the system structure parameter Or excessively increase the calculation step size , the useless frequency components will be enhanced, which will bring interference to the detection; the essence of both the double sampling stochastic resonance and parameter normalization transformation methods is to transform the system structure parameters, but the frequency of the signal to be tested must be known In order to determine the structural parameters of the system

[0004] Therefore, large-signal and small-signal stochastic resonance processing cannot be directly implemented using a bistable system

To achieve large-signal stochastic resonance, it is necessary to add a modulation circuit on the basis of the small-signal stochastic resonance system or redesign the stochastic resonance system with different structural parameters, which greatly increases the hardware cost

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