Method for detecting closely spaced frequency components of non-stationary signals

Abstract

The invention discloses a method for detecting closely spaced frequency components of non-stationary signals. The method comprises the following steps: performing EMD on non-stationary signals to be detected, and then obtaining the frequency spectrum and limit spectrum of the non-stationary signals to be detected through Hilbert transformation; obtaining the value of each frequency component through processing frequency spectrum graph data, and extracting signals with different frequency components by use of a filtering method; determining whether each frequency component comprises multiple frequency values which are not separated through AMD; if a frequency aliasing phenomenon occurs in the signals, performing the AMD on the frequency components, and separating signals with similar frequencies; and then performing operation on the separated signals according to a previous step sequence until single-frequency signals are separated, such that it can be ensured that the decomposed signals all have single-frequency components. The method provided by the invention solves the problem that Hilbert-Huang transformation cannot effectively separate signals with two closely spaced frequency components, correct decomposition of signal effective data is ensured, and the signal decomposition precision is improved.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a detection method for closely spaced frequency components of non-stationary signals. Background technique [0002] In the field of signal decomposition processing, the Hilbert-Huang Transformation (HHT) algorithm is proposed to make up for the shortcomings of the traditional time-frequency analysis method. HHT has strong adaptability to the processing of non-stationary signals. . Its core idea is to decompose the time series by EMD first, and then perform Hilbert transform on each component of the signal processing method. HHT can analyze both linear stationary signals and nonlinear non-stationary signals. [0003] Although the Hilbert-Huang transform method can effectively deal with non-stationary signals, not all signals can be effectively decomposed. When dealing with signals with similar frequencies, the function will be greatly weakened. The effective decomposi...

AI Technical Summary

Problems solved by technology

This method overcomes the problem that HHT cannot decompose signals with closely spaced frequency components, but this method needs to determine each frequency component in the signal

[0005] Since the Fourier transform is not suitable for non-stationary signals, although the Hilbert-Huang transform can effectively deal with non-stationary signals, it cannot identify closely spaced frequency components; while the AMD method overcomes the fact that HHT cannot decompose closely spaced frequencies However, AMD is based on Fourier transform, which makes it difficult to detect closely spaced frequency components of non-stationary signals.

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