A Denoising Method Based on Hybrid Empirical Mode Decomposition

Abstract

The invention discloses a denoising method based on hybrid EMD (Empirical Mode Decomposition), and belongs to the technical field of digital signal processing. According to the denoising method disclosed by the invention, EEMD (Ensemble Empirical Mode Decomposition) and MEMD (Multivariate Empirical Mode Decomposition) algorithms are combined, and aiming at a masking signal frequency used in the MEMD algorithm, parameter optimization is carried out, so that mode mixing can be more effectively eliminated, and a denoising effect is better; and according to the denoising method, targeted optimization is further carried on an additive noise amplitude in the EEMD, filtering thresholds of noise related IMF (Spurious modes) and filtering thresholds of noise IMF, so that an integral denoising effect is further improved. Compared with the prior art, the method disclosed by the invention can more effectively eliminate mode mixing, and has a better denoising effect.

Description

technical field [0001] The invention relates to a denoising method, in particular to a denoising method based on hybrid empirical mode decomposition (EMD for short), and belongs to the technical field of digital signal processing. Background technique [0002] EMD and its derived EEMD (Ensemble Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition) and masking signal EMD (Mask Signal Method Empirical Mode Decomposition, hereinafter referred to as MEMD) are signal processing methods developed in recent years. It decomposes based on the signal time scale and is suitable for nonlinear and non-stationary signal processing. Since it does not need to determine the subjective experience parameter settings such as basis functions and decomposition layers, in some cases, it has better decomposition than wavelet transform. Effect. Even so, EMD has its own problems and drawbacks. The problems of EMD mainly include endpoint effect and mode mixing. Although EEMD can solv...

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Problems solved by technology

The selection of the amplitude and frequency parameters of the masking signal of MEMD is a key step. Although the reference values ​​are given in the paper, in practical applications, the reference frequency is often lower than the necessary requirements of the masking signal, so the elimination of modal aliasing cannot be achieved. The ideal effect, therefore, for different signals, the results of the recommended parameter calculation are not optimal

IMF filtering mainly includes wavelet threshold filtering, EMD direct filtering (EMD-DT) and EMD interval filtering (EMD-IT) and related derivative filtering methods, among which the error of wavelet threshold coefficient is large, and it is not easy to find out in practice; EMD-DT The continuity of the signal cannot be guaranteed, and it is not good to filter signals for signals with multiple amplitude ranges; although EMD-IT can guarantee the continuity of the signal, it cannot guarantee the filtering performance of low-order IMF multi-amplitude signals

All in all, EMD-IT filtering has two main characteristics. First, after finding the relative (noise-related) IMF, the low-order "noise" IMF is directly discarded without considering the effective components contained in it; second, the filtering threshold is not automatic. Adaptation, a single threshold strategy has a large error for multi-amplitude signal filtering

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