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Time frequency decomposition and reconstruction method of generalized S transform signals for synchronous extrusion

A technology of synchronous extrusion and time-frequency decomposition, applied in the field of signal processing, can solve problems such as the inability to automatically adjust the resolution, suboptimal spectrum resolution, and poor non-stationary signal processing effect

Pending Publication Date: 2017-10-03
CHENGDU UNIVERSITY OF TECHNOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Among them, STFT cannot automatically adjust the resolution according to the frequency transformation of the signal because of its fixed window length, and the processing effect on non-stationary signals is poor; CWT has the characteristics of multi-resolution analysis through the time-scale analysis of the signal, but the wavelet-based Difficult to choose; ST can better describe the components in the signal, and can realize lossless inverse transformation, but its basic wavelet function is fixed, which limits its application; the generalized S transform is obtained by deriving ST, and its basic wavelet The function can be adjusted according to the needs of the problem to be dealt with, which has better practicability and flexibility in the application, but affected by the uncertainty principle, the resolution of the spectrum in the generalized S-transform is not optimal

Method used

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  • Time frequency decomposition and reconstruction method of generalized S transform signals for synchronous extrusion
  • Time frequency decomposition and reconstruction method of generalized S transform signals for synchronous extrusion
  • Time frequency decomposition and reconstruction method of generalized S transform signals for synchronous extrusion

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Effect test

Embodiment 1

[0083] Embodiment 1: see figure 1 , a method for time-frequency decomposition and reconstruction of a synchronously squeezed generalized S-transform signal, comprising the following steps:

[0084] (1) Acquire signal x(t);

[0085] (2) Carry out four-parameter generalized S-transformation on the signal x(t) using the following formula,

[0086]

[0087] Wherein, the four parameters are: basic wavelet amplitude A, energy decay rate α (α>0), energy delay time β, basic wavelet video frequency f 0 ; f is the frequency of the four-parameter generalized S-transform, and b is the time-axis displacement parameter of the four-parameter generalized S-transform;

[0088] (3) For the four-parameter generalized S-transform result GST x (f, b) modulo, to obtain the energy of each time-frequency point, so as to obtain the generalized S-transform time spectrum,

[0089] S GST =|GST x (f,b)|;

[0090] (4) Based on the four-parameter generalized S-transform result GST obtained in step...

Embodiment 2

[0098] Example 2: see figure 2 — Figure 8 , the synchronous extrusion generalized S-transform can adjust the change trend of the basic wavelet function by adjusting the four parameters in the generalized S-transform according to the actual needs, so as to adapt to the analysis and processing of specific signals. The four parameters are basic wavelet amplitude, energy decay rate, energy delay time and basic wavelet video frequency. Figure 2-Figure 8 , shows the generalized S-transform window function of parameters A, α, β under different value conditions, and illustrates the specific role of each parameter.

[0099] figure 2 The selected parameters are A=2, α=0.5, β=1, image 3 The selected parameters are A=2, α=2, β=1. Comparing the two, we can see that the value of α determines the window size of the window function, and the size of the window is inversely proportional to the value of α. The smaller the value of α, the larger the window, and the value of α The larger ...

Embodiment 3

[0101] Embodiment 3: see Figure 9 — Figure 20 .

[0102] The FM signal is a recognized model for testing the time-frequency aggregation performance of the time-frequency distribution. Figure 12 The composite signal shown is composed of Figure 9 , Figure 10 The 2 FM signals shown and Figure 11 The signal-to-noise ratio shown is the superposition of 6dB Gaussian white noise, so we will Figure 12 The composite signal shown in is used as the signal x(t) in step (1), and the specific implementation method is as follows:

[0103] (1) Obtain signal x(t), said x(t) is a composite signal, by Figure 9 , Figure 10 The 2 FM signals shown and Figure 11 The signal-to-noise ratio shown is the superposition of 6dB Gaussian white noise;

[0104] (2) Carry out four-parameter generalized S-transformation on the signal x(t) using the following formula,

[0105]

[0106] Wherein, the four parameters are: basic wavelet amplitude A, energy decay rate α (α>0), energy delay time...

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Abstract

The invention discloses a time frequency decomposition and reconstruction method of generalized S transform signals for synchronous extrusion. Firstly, the four parameter generalized S transform is conducted on a signal, the variation trend of a basic wavelet function is adjusted by the adjustment of the basic wavelet amplitude, energy attenuation rate, energy delay time and basic wavelet video rate; secondly, the modulus of the result of the four parameter generalized S transform is obtained, and the energy of each time-frequency point is obtained, thus the time-frequency spectrum is obtained; thirdly, the instantaneous frequency is obtained by the four parameter generalized S transform of the signal; fourthly, the frequency set after the generalized S transform is set as the central frequency set, each time frequency point corresponding to the instantaneous frequency close to the interval of the central frequency is extruded to the central frequency point, and the generalized S transform of synchronous extrusion is obtained; finally, the inverse transformation formula of generalized S transform for synchronous extrusion is deduced. The generalized S transform of synchronous extrusion has the advantages of synchronous extrusion transform and generalized S transform, and is a time-frequency decomposition and reconstruction method for high precision signals.

Description

technical field [0001] The invention relates to the field of signal processing, and is a high-precision time-frequency decomposition and reconstruction method for generalized S-transform signals of synchronous extrusion. Background technique [0002] Signals are unary or multivariate functions that carry information. In real life, we are exposed to a large number of signals every day, for example, the number of people seeing a doctor in a certain hospital every day, the number of sunspots on the sun every year, etc. As a branch of information science, signal processing has penetrated into various fields of science and technology, and even penetrated into many fields of social science. Signal processing takes Fourier analysis as the theoretical basis to study the transformation, filtering and feature extraction of signals. The signal is a function of time, and Fourier analysis provides us with a new perspective to look at the signal, that is, to look at the signal from the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/14
CPCG06F17/148
Inventor 陈辉陈旭平卢柃岐陈学华胡英徐丹康佳星陈元春周心悦
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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