期刊
IEEE SIGNAL PROCESSING MAGAZINE
卷 30, 期 6, 页码 74-86出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MSP.2013.2267931
关键词
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资金
- EPSRC [EP/K025643/1]
- NSF [AGS-1139479,]
- [NSC 99-2911-I-008-100]
- Div Atmospheric & Geospace Sciences
- Directorate For Geosciences [1139479] Funding Source: National Science Foundation
- Engineering and Physical Sciences Research Council [EP/K025643/1] Funding Source: researchfish
- EPSRC [EP/K025643/1] Funding Source: UKRI
This article addresses data-driven time-frequency (T-F) analysis of multivariate signals, which is achieved through the empirical mode decomposition (EMD) algorithm and its noise assisted and multivariate extensions, the ensemble EMD (EEMD) and multivariate EMD (MEMD). Unlike standard approaches that project data onto predefined basis functions (harmonic, wavelet) thus coloring the representation and blurring the interpretation, the bases for EMD are derived from the data and can be nonlinear and nonstationary. For multivariate data, we show how the MEMD aligns intrinsic joint rotational modes across the intermittent, drifting, and noisy data channels, facilitating advanced synchrony and data fusion analyses. Simulations using real-world case studies illuminate several practical aspects, such as the role of noise in T-F localization, dealing with unbalanced multichannel data, and nonuniform sampling for computational efficiency.
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