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Fast Multivariate Empirical Mode Decomposition
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DOI：10.1109/ACCESS.2018.2877150.png)
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摘要
En 中文
The multivariate empirical mode decomposition (MEMD) has been pioneered recently for adaptively processing of multichannel data. Despite its high efficiency on time-frequency analysis of nonlinear and nonstationary signals, high computational load and over-decomposition have restricted wider applications of MEMD. To address these challenges, a fast MEMD (FMEMD) algorithm is proposed and featured by the following contributions: 1) A novel concept, pseudo direction-independent multivariate intrinsic mode function (IMIMF) which allows the interchange of sifting and projection operations, is defined for the purpose of developing FMEMD; 2) FMEMD is computationally efficient. Compared with MEMD, the number of time-consuming sifting operations reduces from K . p to K for each iteration, where K and p denote the number of projection directions and signal dimension, respectively; 3) FMEMD is consistent with EMD in terms of the dyadic filter bank property; and 4) FMEMD is more effective in working at low sampling rate. Validity of the raised approach is demonstrated on a wide variety of real world applications.
Keyword:
Multivariate empirical mode decomposition
multivariate intrinsic mode function
fast MEMD
computational load
filter bank
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3.6
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9.8W
被引数:
29.4W
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