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Maximum Error in Discrete EMD Decomposition of Periodic Signals

机译:周期信号离散EMD分解的最大误差

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The present investigation concerns the recently developed Hilbert-Huang transformation. This technique is expected to decompose time-dependant data series into its individual characteristic oscillations with the so-called Empirical Mode Decomposition (EMD). EMD is capable to decompose empirically any complex set of data into a finite number of Intrinsic Mode Functions (IMF). The aim of the present experimental study is to contribute to a better understanding of particular aspect of EMD. We consider the spectral analysis of the IMF components of digital signals. We show that errors of EMD decomposition of sine wave are in fact new frequency components called artefacts. These artefacts depend on a signal frequency and sampling frequency. In this paper, artefacts are explored and maximum error is analysed in spectral domain.
机译:本研究涉及最近开发的希尔伯特-黄(Hilbert-Huang)变换。期望该技术通过所谓的经验模式分解(EMD)将与时间相关的数据序列分解成其各个特征振荡。 EMD能够根据经验将任何复杂的数据集分解为有限数量的本征模式函数(IMF)。本实验研究的目的是有助于更好地理解EMD的特定方面。我们考虑对数字信号的IMF分量进行频谱分析。我们表明,正弦波EMD分解的误差实际上是称为伪像的新频率分量。这些伪影取决于信号频率和采样频率。本文探讨了伪像,并分析了光谱域中的最大误差。

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