High-precision time-frequency analysis method

Abstract

The invention provides a high-precision time-frequency analysis method, and belongs to the field of digital signal processing. The method comprises the following steps of: (1)inputting original digital signals x(t), then carrying out empirical mode decomposition (EMD)to the original digital signals x(t), decomposing the original digital signals x(t) into sets of intrinsic mode function (IMF)components, namely ci(t) is the IMF component; (2) carrying out matching pursuit (MP)decomposition to each IMF component ci(t) respectively to obtain a time-frequency spectrum of each IMF component; and (3) carrying out mutual superposition to the time-frequency spectrum of each IMF component obtained in the step (2) to obtain the time-frequency spectrum of a high time-frequency resolution of the original digital signals x(t). The high-precision time-frequency analysis method disclosed by the invention avoids inaccuracy of time-frequency atoms when MP decomposition matching is implemented for composite signals directly, and MP decomposition is implemented for each IMF component, thereby being capable of obtaining the time-frequency spectrum with higher accuracy and time-frequency resolution and richer information.

Description

technical field [0001] The invention belongs to the field of digital signal processing, and in particular relates to a high-precision time-frequency analysis method for digital signals. Background technique [0002] Time-frequency analysis is a traditional method for analyzing non-stationary signals. It can transform a one-dimensional time-domain or frequency-domain signal into a two-dimensional time-frequency plane, obtain the time-frequency spectrum of the signal, and extract and distinguish signal components in the time-frequency domain. , to understand the characteristics of the signal more clearly. Time-frequency analysis methods are generally divided into linear time-frequency analysis methods, bilinear time-frequency analysis methods and parametric time-frequency analysis methods. Typical linear time-frequency analysis methods include short-time Fourier transform, wavelet transform, S-transform, etc.; because linear time-frequency analysis methods are constrained by ...

AI Technical Summary

Problems solved by technology

Then with continuous searching, the matched time-frequency atoms become more and more distorted and meaningless

At the same time, it is meaningless to predict the time, frequency and phase of time-frequency atoms directly by using the attributes calculated by the Hilbert transform of nonlinear composite signals, and it is impossible to accurately locate the time and frequency of single-component signals and other related information , resulting in an inaccurate time-spectrum of the final calculation

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