Method for acquiring time-frequency distribution based on self-filtering frequency conversion experience modal decomposition

An empirical mode decomposition and time-frequency distribution technology, which is applied in the field of signal processing, can solve the problem of low accuracy of time-frequency distribution detection signal parameters, and achieve the effect of suppressing mode aliasing, small error and improved accuracy

Active Publication Date: 2018-06-19
HARBIN INST OF TECH
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Problems solved by technology

[0003] In order to solve the problem of the prior art empirical mode decomposition processing multi-component mixed signals with intersecting instantaneous frequencies to produce modal aliasing, resulting in low accuracy of time-frequency distribution detection signal parameters, the present invention provides an empirical mode based on self-filtering frequency conversion Method of Decomposing to Obtain Time-Frequency Distribution

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  • Method for acquiring time-frequency distribution based on self-filtering frequency conversion experience modal decomposition
  • Method for acquiring time-frequency distribution based on self-filtering frequency conversion experience modal decomposition
  • Method for acquiring time-frequency distribution based on self-filtering frequency conversion experience modal decomposition

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specific Embodiment approach 1

[0020] Specific embodiment one: combination figure 1 To explain this embodiment, the method for obtaining time-frequency distribution based on self-filtering and variable frequency empirical modal decomposition provided in this embodiment specifically includes the following steps:

[0021] Step 1: Perform empirical mode decomposition on the original signal x(t) to obtain several eigenmode functions;

[0022] Among them, each eigenmode function obtained after empirical mode decomposition is called the component of the signal before decomposition;

[0023] Step 2: Calculate the similarity coefficients between each eigenmode function and x(t), remove the false components according to the calculated similarity coefficients, and obtain the remaining N eigenmode functions IMF 1 ,IMF 2 ,...,IMF N , N≥1; set the variable i=0;

[0024] Step three, use Hilbert transform to IMF 1 Perform frequency reduction processing, and perform empirical mode decomposition on the frequency-reduced signal to ob...

specific Embodiment approach 2

[0031] Specific embodiment two: this embodiment is different from specific embodiment one in that the threshold S in step five is 1 Is 0.05, S 2 Is 0.001.

[0032] The other steps and parameters are the same as in the first embodiment.

specific Embodiment approach 3

[0033] Specific implementation manner three: such as figure 2 As shown, the difference between this embodiment and the first or second embodiment is that the specific process of performing empirical mode decomposition on the signal x(t) includes:

[0034] (1) Set the intermediate variable j=0, and set x′(t)=x(t);

[0035] (2) Find the maximum value and minimum value of the signal x′(t), and respectively fit the maximum value point and the minimum value point, and determine the maximum value envelope u( t) and the minimum envelope l(t) to obtain all the extreme points between the maximum envelope u(t) and the minimum envelope l(t);

[0036] (3) Use formula (1) to calculate the average value of the maximum value envelope u(t) and the minimum value envelope l(t), denoted as m 1 (t):

[0037]

[0038] After that, subtract the resulting m from the signal x′(t) 1 (t), and denoted as d 1 (t), as in formula (2):

[0039] d 1 (t)=x′(t)-m 1 (t) (2)

[0040] If d 1 (t) To meet the requirements of...

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Abstract

The invention provides a method for acquiring time-frequency distribution based on self-filtering frequency conversion experience modal decomposition, belongs to the signal processing technology fieldand especially relates to the method for acquiring the time-frequency distribution. The method is characterized by using experience modal decomposition (EMD) and Teager conversion to acquire the spectrogram of the time-frequency distribution; using an experience modal to decompose a self-filtering characteristic; and adopting a continuous frequency reducing / frequency increasing method to restraina modal aliasing problem generated in an experience modal decomposition process, and acquiring a good time frequency distribution map. In the prior art, during experience modal decomposition processing, instantaneous frequencies are crossed and multi-component mixing signals generate modal-aliasing so that time frequency distribution detection signal parameter precision is low. In the invention,the above problem is solved. The method can be used for the time frequency analysis of signals.

Description

Technical field [0001] The invention belongs to the technical field of signal processing, and specifically relates to a method for obtaining a time-frequency distribution. Background technique [0002] In the field of time-frequency analysis, in order to obtain the time-frequency characteristics of the signal, the methods often used include short-time Fourier transform, fractional Fourier transform, wavelet transform and Winger-Ville transform (WVD transform). The core is Fourier transform. Among them, Winger-Ville transform has been widely used because of its better time-frequency aggregation. The WVD transform can be regarded as the Fourier transform of the signal autocorrelation function, and the essence of the autocorrelation function is a convolution process, and then use the Fourier transform to transform from the time domain to the frequency domain to obtain the aggregation Higher time-frequency distribution. However, when there are multi-component signals or nonlinear ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06F2218/02G06F2218/00
Inventor 赵雅琴张宇鹏吴龙文王昭李锦江
Owner HARBIN INST OF TECH
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