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Empirical mode decomposition as a time-varying multirate signal processing system

机译:经验模式分解作为时变多速率信号处理系统

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Empirical mode decomposition (EMD) can adaptively split composite signals into narrow subbands termed intrinsic mode functions (IMFs). Although an analytical expression of IMFs extracted by EMD from signals is introduced in Yang et al. (2013), it is only used for the case of extrema spaced uniformly. In this paper, the EMD algorithm is analyzed from digital signal processing perspective for the case of extrema spaced nonuniformly. Firstly, the extrema extraction is represented by a time-varying extrema decimator. The nonuniform extrema extraction is analyzed through modeling the time-varying extrema decimation at a fixed time point as a time-invariant decimation. Secondly, by using the impulse/summation approach, spline interpolation for knots spaced nonuniformly is shown as two basic operations, time-varying interpolation and filtering by a time-varying spline filter. Thirdly, envelopes of signals are written as the output of the time-varying spline filter. An expression of envelopes of signals in both time and frequency domain is presented. The EMD algorithm is then described as a time-varying multirate signal processing system. Finally, an equation to model IMFs is derived by using a matrix formulation in time domain for the general case of extrema spaced nonuniformly.
机译:经验模态分解(EMD)可以将复合信号自适应地分成狭窄的子带,称为固有模式函数(IMF)。尽管在Yang等人中介绍了由EMD从信号中提取的IMF的解析表达式。 (2013年),仅用于极端间距均匀的情况。本文从数字信号处理的角度分析了极端值不均匀情况下的EMD算法。首先,极值提取由时变极值抽取器表示。通过将固定时间点的时变极值抽取建模为时不变抽取,来分析非均匀极值抽取。其次,通过使用脉冲/求和方法,将不均匀间隔的节点的样条插值显示为两个基本操作,即时变插值和时变样条滤波器的滤波。第三,信号的包络被写为随时间变化的样条滤波器的输出。给出了时域和频域中信号包络的表达。然后将EMD算法描述为时变多速率信号处理系统。最后,对于极值不均匀分布的一般情况,通过在时域中使用矩阵公式来导出用于建模IMF的方程。

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