向量值经验模分解（VEMD）的一种新视角
A different view on the vector-valued empirical mode decomposition (VEMD)
AI总结：
本文提出一种新的向量值经验模分解方法，通过改进的反投影方法提升多维信号处理性能，实验表明其优于现有方法。
摘要：
经验模分解（EMD）通过提供非线性和非平稳信号的多尺度时频表示而获得声誉。为将此方法扩展到多维空间中的向量值信号（VvS），最近设计了多变量EMD（MEMD），通过集合投影提取不同投影方向下的局部极值位置（LELs）。与MEMD不同，本文提出的向量值EMD（VEMD）采用新的反投影方法，从投影空间中的1-D包络线插值得到向量值包络线（VvEs）。考虑到典型的4-D坐标（3-D位置和时间），通过数值模拟显示VEMD在性能上优于现有方法。
英文摘要：
The empirical mode decomposition (EMD) has achieved its reputation by providing a multi-scale time-frequency representation of nonlinear and/or nonstationary signals. To extend this method to vector-valued signals (VvS) in multidimensional (multi-D) space, a multivariate EMD (MEMD) has been designed recently, which employs an ensemble projection to extract local extremum locations (LELs) of the given VvS with respect to different projection directions. This idea successfully overcomes the problems of locally defining extrema of VvS. Different from the MEMD, where vector-valued envelopes (VvEs) are interpolated based on LELs extracted from the 1-D projected signal, the vector-valued EMD (VEMD) proposed in this paper employs a novel back projection method to interpolate the VvEs from 1-D envelopes in the projected space. Considering typical 4-D coordinates (3-D location and time), we show by numerical simulations that the VEMD outperforms state-of-art methods.
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