A Weak Signal Detection Method Based on Parameter Compensated Multistable Stochastic Resonance System

Abstract

The invention discloses a method for detecting a weak signal on the basis of a parameter compensation multistable random resonance system. The method comprises the following steps of (1) performing parameter compensation on a noise-containing weak signal, i.e. adding an amplifying link in a multistable Langevin equation, to offset the trend that an amplitude value is smaller after the signal passes through a multistable system, so that the method can be used for detecting a high-frequency weak signal; (2) inputting the compensated signal into the multistable system, wherein the multistable system is higher than a bistable system in detectability and can be used for detecting the weak signal with lower signal to noise ratio; (3) performing envelope demodulation analysis on the output signal of the multistable system, and analyzing an envelope diagram to extract the characteristics of the weak signal, so that detection on the weak signal is finished. By the method, the weak signal can be well extracted under the condition of extremely low signal to noise ratio, a low-frequency weak signal and a medium-high frequency weak signal can also be detected under the condition that the signal frequency is not known, and the signal processing ability is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a method for extracting low-frequency and medium-high frequency weak signals under low signal-to-noise ratio. Background technique [0002] Weak signals are low-energy signals submerged in the background of strong noise. In many fields such as mechanical fault diagnosis, communication, seismic exploration, oil exploration wells, and biomedicine, it is necessary to detect weak signals to extract useful signals. Therefore, the detection of weak signals has always been Research hotspots. [0003] The existing weak signal detection technology mainly obtains useful signals by eliminating and suppressing noise. While removing noise, it will inevitably weaken useful signals, and can only be used for weak signal detection with high signal-to-noise ratio. The weak signal is extracted under the signal-to-noise ratio, and the effect is not obvious, which cannot meet t...

AI Technical Summary

Problems solved by technology

However, when actually dealing with weak signals with extremely low signal-to-noise ratios, the effect of the bistable stochastic resonance system often fails to meet our expected goals, and it is easy to cause inaccurate diagnostic results and even misdiagnosis.

[0006] To sum up, the current method of parameter compensation bistable stochastic resonance system to extract weak signals cannot meet people's requirements

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