Abstract
In order to obtain new ways of weak signal detection, we analyze the motion of Duffing oscillator in the case of different input state by solving the Duffing equation firstly, and then elaborate the basic principles of weak signal detection based on Duffing oscillator phase-change characteristics. Further illustrate the experiment that shows how to achieve the weak signal detection with Duffing oscillator based on virtual instrument technology, then discuss the impact on signal detection coming from Gaussian white noise and how to implement weak signal detection with noises. The results show that, Duffing oscillator not only can be effective on detection of weak signal in the background of strong noises, but also has high cost performance to achieve it with virtual instrument technology. Compared with existing methods, it can greatly improve the detection results and has broad application.
Highlights
Weak signal detection is an important part of information technology and has widespread applications
As information technology continues to evolve, people are exploring a variety of weak signal detection methods: detection methods based on wavelet transformation, detection based on fuzzy mathematics, artificial neural network-based detection, and in recent years, Duffing oscillator detection based on chaos theory has been considered and is achieving a lot of the results
When the system is in the great cycle state, output signals in the time domain pass through the zero point with the same interval, and when the system is in a chaotic state, the output signal zerocrossing time interval is variable, so we can determine that the system is in a large-scale periodic state or in a chaotic state by comparing the output signal zerocrossing time intervals
Summary
Weak signal detection is an important part of information technology and has widespread applications. If the signal spectrum is narrow and the distributed noise spectrum is wide, using narrow-band filters that can only pass narrow-band signals and filter out the noise outside the pass band significantly improved the signal to noise ratio to detect the useful signal buried in the noise. This method is easy to implement, but the instability of the filter center frequency has a greater impact in a narrow-band, so its application test results are subject to certain restrictions
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