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A skewness-based short-time-time-frequency transformation method with adaptive variable window length

An adaptive, long-short technology, applied in special data processing applications, complex mathematical operations, design optimization/simulation, etc., can solve the problem that STLVT cannot adjust the window length due to signal characteristics, and achieve excellent performance

Active Publication Date: 2021-06-01
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0011] In view of the above existing 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 method, which is called Adaptive Window Lv Transform (Adaptive Window Lv Transformation) based Lv Transform, AWLT)

Method used

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  • A skewness-based short-time-time-frequency transformation method with adaptive variable window length
  • A skewness-based short-time-time-frequency transformation method with adaptive variable window length
  • A skewness-based short-time-time-frequency transformation method with adaptive variable window length

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 in particular relates to a skewness-based self-adaptive variable window length-short time-time-frequency transformation technology, aimed at nonlinear frequency modulation signals. The present invention uses skewness to control the adaptive process of the length of the window function: first take the starting point of the window length as the starting point of the signal, take the window length as the maximum, and gradually reduce the window length based on the skewness control until the adaptive result of the window length is obtained, and then The starting point of windowing is moved forward (N 1 / 4) points for windowing; take this windowing starting point backward (3N 1 / 4) points are used as the starting point of the next windowing; after all windowing is completed, the results of each time are summarized into a time-frequency diagram. The invention solves the problem that the signal aggregation degree and the resolution are not high enough when the existing self-adaptive time-frequency transformation analyzes multi-component frequency modulation signals.

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 window length-short time-time-frequency transformation method, 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 signal, e represe...

Claims

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Application Information

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