Wavelet transform signal frequency spectrum measuring analytical method adopting variable band-width parameter

Abstract

Disclosed is a method for measuring and analyzing signal spectra, which can make a frequency spectrum analysis of transient and non-periodic signals and still keep a better frequency resolution at the high frequency part of the signal spectra by adopting variable bandwidth parameters in the Morlet wavelet transform. The method comprises the following steps of: recording the measured and analyzed signals firstly, then calculating the wavelet transform with variable bandwidth parameters of the measured and analyzed signals, then summing wavelet transform coefficients at different frequency points along a time axis and calculating the average value, and finally obtaining the frequency spectrum curve of the measured and analyzed signals.

Description

technical field [0001] The invention relates to a signal spectrum measurement and analysis method, in particular to a signal spectrum measurement and analysis method using MORLET continuous wavelet transform. Background technique [0002] In recent years, with the increase of the clock frequency of digital circuits, the jitter, transient and aperiodicity of the signal make the traditional signal spectrum measurement and analysis technology, Fourier Transform (FOURIER TRANSFORM), difficult to meet the requirements. Because the Fourier transform assumes that the signal to be measured and analyzed is periodic and steady-state, even if a certain signal mutation occurs only once, the Fourier transform assumes that it must reappear forever, resulting in a large measurement and analysis error. [0003] The wavelet transform, because it uses a set of generative functions of different widths to measure the tested signal, is suitable for both periodic signals and transient and non-per...

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Problems solved by technology

[0004] However, if wavelet transform is used for signal spectrum analysis to overcome the weakness that Fourier transform cannot be used for transient and non-periodic signal analysis, the above-mentioned advantages of automatic resolution adjustment of wavelet transform become disadvantages: as the measurement frequency increases , the frequency resolution of the wavelet transform gradually decreases, and the frequency band gradually widens

Because wavelet transform averages all frequency components in the frequency band, it makes it difficult to distinguish the frequency at the high end of the spectrum, which hinders the application of wavelet transform in signal spectrum measurement and analysis.

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