EMD based normalization stochastic resonance weak signal detection under oversampling

A technology of empirical mode decomposition and weak signal detection, applied in the field of signal processing, which can solve the problems of signal weakening and inaccurate detection.

Inactive Publication Date: 2018-04-03
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Although these methods can eliminate noise, at the same time the useful signal will be weakened, making the detection inaccurate

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  • EMD based normalization stochastic resonance weak signal detection under oversampling
  • EMD based normalization stochastic resonance weak signal detection under oversampling
  • EMD based normalization stochastic resonance weak signal detection under oversampling

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[0015] The implementation of the present invention will be further described below in conjunction with the accompanying drawings and specific examples. figure 1 It is a stochastic resonance process flow diagram of empirical mode decomposition under oversampling. The specific steps are: first decompose the signal mixed with Gaussian noise through EMD, and then select the decomposition component with the target signal, and use parameter normalization transformation to transform the large frequency signal Make it meet the small parameter signal of the adiabatic approximation theory, and then send the small parameter signal into the stochastic resonance system, and finally get the signal spectrum output by the stochastic resonance system, the spectrum change diagram before and after observation, and the signal-to-noise ratio gain and accuracy to reach the detection signal the goal of.

[0016] figure 2 It is the empirical mode decomposition diagram under low sampling, which adop...

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Abstract

The invention provides an EMD (Empirical Mode Decomposition) based normalization bistable stochastic resonance weak signal detection under oversampling and belongs to the field of signal processing technology. According to the invention, EMD and normalization stochastic resonance are combined into a new system. Signal mode component is superposed for parameter normalization on the basis of EMD ofnoisy signals, so that noisy signals are enabled to meet requirements for stochastic resonance small parameters and are sent to a stochastic resonance system. Whether obvious peaks exist or not is observed through observing a spectrogram and the average SNR gain is calculated. The SNR increases to some extent after EMD and increases once more after stochastic resonance. The total increase is greater than that of using stochastic resonance independently. The EMD based normalization bistable stochastic resonance weak signal detection under oversampling disclosed by the invention has universal meaning in practical application, reduces parameter adjustment complexity and solves a problem that large frequency cannot meet requirements for stochastic resonance.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to normalized stochastic resonance weak signal detection of empirical mode decomposition under oversampling. Background technique [0002] Weak signals refer to a type of signal that is difficult to identify and detect in various noises, such as underwater acoustic signals, biomedical signals, mechanical failure signals, and seismic signals. Detection technology also starts from the two aspects of filtering noise and using noise. The traditional filtering detection method is to eliminate noise. The commonly used methods include empirical mode decomposition (EMD), wavelet denoising, and filter. Although these methods can eliminate noise, at the same time the useful signal will be weakened, making the detection inaccurate. The subsequent stochastic resonance is an effective way to convert high-frequency energy into low-frequency energy to detect weak signals, enhance signal...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01D21/00
CPCG01D21/00
Inventor 贺利芳曹莉易甜郝怡曼张天骐张刚
Owner CHONGQING UNIV OF POSTS & TELECOMM
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