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Skewness-based self-adaptive window-variable long-short-time time frequency transformation technology

A transformation technology and self-adaptive technology, applied in special data processing applications, complex mathematical operations, instruments, etc., can solve the problem that STLVT cannot adjust the window length, etc., and achieve excellent technical performance

Active Publication Date: 2018-11-30
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] In view of the above-mentioned problems or deficiencies, in order to solve the problem that STLVT cannot adjust the window length according to the signal characteristics, the present invention provides a skewness-based adaptive variable window length short-time-time-frequency transformation technology, which is called Adaptive Window Lv Transform (Adaptive Window Lv Transformation) based Lv Transform, AWLT)

Method used

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  • Skewness-based self-adaptive window-variable long-short-time time frequency transformation technology
  • Skewness-based self-adaptive window-variable long-short-time time frequency transformation technology
  • Skewness-based self-adaptive window-variable long-short-time time frequency transformation technology

Examples

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Effect test

example 1

[0053] Example 1: Under the computer MATLAB environment, a two-component simulation signal is generated according to the following formula: each parameter of frequency modulation is f 1 =-30Hz, f 2 =20Hz, γ 1 =0.6Hz / s, γ 2 =20Hz / s; sampling frequency f s =256Hz, signal sampling points N s =8192.

[0054]

[0055] In this example, the ratio Q of reducing the window length each time is set to 0.5.

[0056] figure 2 (a) is the original time-frequency diagram of the input signal. figure 2 (b) is a time-frequency diagram using the present invention, namely AWLT. by comparison figure 2 (a) and figure 2 (b) It can be seen that the original time-frequency diagram of the input signal and the time-frequency diagram of the AWLT are highly overlapped, indicating that the present invention can handle such signals well.

[0057] In order to show the advantages of the present invention over STLVT, another example is given below.

example 2

[0058] Example 2: In the computer MATLAB environment, a single-component simulation signal is generated according to the following formula: each parameter of frequency modulation is f 1 =-5Hz, γ 1 =6Hz / s; sampling frequency f s =256Hz, signal sampling points N s =8192.

[0059]

[0060] In this example, the ratio Q of reducing the window length each time is set to 0.8.

[0061] image 3 (a) is the original time-frequency diagram of the input signal. image 3 (b) is a time-frequency diagram of processing an input signal with the present invention, ie AWLT. image 3 (c) is the time-frequency diagram of STLVT with a window length of 1536 points. We can see from the figure that the time-frequency curve of AWLT is relatively smooth, and the waveform is very close to the original waveform. The time-frequency curve of STLVT is not smooth enough, and the waveform is not close enough to the original waveform. This is caused by the fact that the fixed window length used by ST...

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Abstract

The invention belongs to the field of time frequency analysis in signal processing, and particularly relates to skewness-based self-adaptive window-variable long-short-time time frequency transformation technology which is aims at nonlinear frequency modulation signals. The technology includes: using skewness to control self-adaptive process of window function length: taking a window length starting point as a signal starting point, adopting maximum window length, and controlling on the basis of the skewness to gradually reduce the window length until a self-adaptive result of window length isacquired, and forwards moving a windowing starting point by (N1 / 4) points for windowing; taking a point of (3N1 / 4) points after the windowing starting point as a starting point for windowing of the next time; after all windowing is completed, summarizing results to form a time frequency graph. By the method, the problem that signal aggregation level and resolution are not high enough when existing self-adaptive time frequency transformation is adopted to analyze multi-component frequency modulation signals is solved.

Description

technical field [0001] The invention belongs to the field of time-frequency analysis in signal processing, and in particular relates to a skewness-based self-adaptive variable window length-short time-time-frequency transformation technology, which is mainly aimed at nonlinear frequency modulation signals. Background technique [0002] FM signal refers to a signal whose frequency changes continuously over a continuous period, and is widely used in various information systems including radar, sonar and communication. According to different forms of signal frequency change, frequency modulation signals can be divided into linear frequency modulation signals and nonlinear frequency modulation signals. [0003] A chirp signal can be expressed by the following general formula: [0004] [0005] Where s represents the input signal, t represents the time variable, K represents the number of components of the signal, A k Represents the amplitude of the kth component of the sign...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/14G06F17/50
CPCG06F17/14G06F30/20
Inventor 罗钐徐起
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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