Weak signal detection and recovery method based on first-order linear system

A weak signal detection and linear system technology, applied in the direction of measuring devices, instruments, etc., can solve the problem of less research on weak signals

Inactive Publication Date: 2016-12-07
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0004] Most stochastic resonance systems detect the frequency of weak signals

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  • Weak signal detection and recovery method based on first-order linear system
  • Weak signal detection and recovery method based on first-order linear system
  • Weak signal detection and recovery method based on first-order linear system

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Embodiment Construction

[0036] The implementation of the present invention will be further described below in conjunction with the accompanying drawings and specific examples.

[0037] Step 1: The low signal-to-noise ratio signal mixed with noise is adaptively processed by a first-order linear system;

[0038] Select noisy signals with different signal-to-noise ratios to drive the first-order linear system, use the above algorithm, select the average cross-correlation coefficient p as the system performance index, and study its stochastic resonance phenomenon and its variation with various parameters. In the experiment, keep the amplitude A of the weak sinusoidal signal to be tested = 0.1v, the frequency f = 0.01Hz, and the sampling frequency f s =5Hz, take the number of points N=4000. Take D ∈ [0.1,4], α ∈ (0,2], D takes 0.1 as the initial value, the step size is 0.1, α takes 0.02 as the initial value, and the step size is 0.02 and changes at the same time, calculate the corresponding p value, and sim

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Abstract

The invention relates to a weak signal detection and recovery method based on a first-order linear system and belongs to the technical field of signal processing. The method researches a generalized stochastic resonance phenomenon of a first-order linear system structure parameter a by using a four-order Range-Kutta method and using a cross correlation coefficient as an index, then provides a high-frequency and low-frequency weak signal recovery method of the linear system, and finally recovers high-frequency and low-frequency weak periodic signals. It is found by a research that the greater the D is, less and more stable the corresponding cross correlation coefficient p is, the p increases and then decreases with the structure parameter a, and the p does not change with an increase of a and keeps stable when the a increases to a certain value. In an inversion system, the larger the correlation coefficient of a first-order linear system output signal and the low-frequency weak signal to be detected, a recovery effect is better, and high-frequency and low-frequency (multi-frequency) weak signals have the same recovery rule. The method provides forceful guarantee for research of generalized stochastic resonance system, and has important significance to detection and recovery of weak signals in engineering application.

Description

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Claims

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Application Information

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Owner CHONGQING UNIV OF POSTS & TELECOMM
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