Adaptive time-frequency transformation method based on streaming algorithm

A time-frequency transformation and self-adaptive technology, applied in the field of signal processing, can solve problems such as complex calculations and high memory costs, and achieve the effects of avoiding iterative algorithms, flexible parameter selection, and saving memory costs

Active Publication Date: 2021-06-01
JILIN UNIV
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  • Application Information

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Problems solved by technology

[0004] The purpose of the embodiments of the present invention is to provide an adaptive time-frequency transformation method based on a streaming algorithm, which aims to solve the problems of complex calculation and high memory cost in the existing local time-frequency transformation method

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  • Adaptive time-frequency transformation method based on streaming algorithm
  • Adaptive time-frequency transformation method based on streaming algorithm
  • Adaptive time-frequency transformation method based on streaming algorithm

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example 1

[0071] image 3 It is a simple synthetic signal, which contains two interleaved chirp signals, the time sampling interval T is 2ms, and the maximum propagation time is 1s. Figure 4 is the theoretical frequency of the chirp signal. Figure 5 It is the time-spectrum diagram processed by the adaptive time-frequency transformation based on the streaming algorithm. When performing time-frequency transformation, set the window length M to 80, the parameter ε to 0.001, and the frequency sampling interval and frequency range are determined by the Nyquist frequency. from Figure 5 It can be seen that the method of the present invention can intuitively decompose the two chirp signals in the time-frequency spectrum, accurately describe the relationship between frequency and time variation, and effectively reflect the time-frequency characteristics of the signal. Image 6 In order to use the formula (15) to inversely transform the reconstructed signal, Figure 7 is the difference bet...

example 2

[0073] Figure 8 It is the actual seismic data in a certain area, the time length is 4s, the time sampling interval T is 2ms, and the total number of time samples N is 2001. The short-time Fourier transform is usually based on the piecewise fast Fourier transform, and the highest sampling frequency is determined by the Nyquist frequency, that is, So the frequency range is [0,250]Hz, and the frequency sampling interval (N fft =2048 is an integer power of 2), and the total frequency sampling points are 1025, then the frequency spectrum needs to store 2001×2005 data during the short-time Fourier transform. When the highest sampling frequency is greater than the peak frequency of the actual data, the range of the time spectrum higher than the peak frequency of the data does not contain useful information, which wastes storage space. The method of the present invention can select frequency range and frequency sampling interval according to actual conditions, Figure 9 For the...

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Abstract

The invention is applicable to the field of signal processing, and provides an adaptive time-frequency transformation method based on a streaming algorithm, which comprises the following steps of: carrying out sliding time window processing on a real discrete signal to obtain a segmented signal; carrying out fourier series expansion on the segmented signal, and introducing a time-varying Fourier coefficient so that the segmented signal is expressed in a form containing the time-varying Fourier coefficient; and according to the form containing the time-varying Fourier coefficient, adaptively updating the time-varying Fourier coefficient by using a streaming algorithm, and determining the local time-frequency spectrum of the real discrete signal. The mathematical underdetermined problem under the regularization condition is analyzed and calculated, an iterative algorithm in local time-frequency transformation is avoided, and the memory cost in the calculation process is effectively saved; meanwhile, the parameter selection is flexible, the frequency sampling interval and the frequency range can be independently selected, only effective frequency information is reserved, and the data storage space is saved.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to an adaptive time-frequency transformation method based on a stream algorithm. Background technique [0002] Signals in many technical fields are mostly non-stationary signals, such as geophysical exploration, medicine and communication. The traditional Fourier transform can only characterize the global spectral characteristics of the signal, which is not suitable for processing and analyzing non-stationary signals. Time-frequency analysis studies the change law of the frequency information of the signal over time, and can characterize the characteristics of the signal more comprehensively and carefully, which is of great significance to the processing and interpretation of non-stationary signals. [0003] In the 1940s, Gabor proposed the short-time Fourier transform (STFT), which implements segmental Fourier transform by windowing the signal, which can characterize the time-freq...

Claims

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Application Information

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IPC IPC(8): G06F17/12G06F17/14
CPCG06F17/12G06F17/141
Inventor刘洋王青晗刘财李鹏郑植升
OwnerJILIN UNIV