IMF mode demixing in EMD for jitter analysis
IMF mode demixing in EMD for jitter analysis
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DOI：
10.1016/j.jocs.2017.04.008
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发表时间：
2017-09-01
影响因子：
3.3
通讯作者：
Wozniak, Marcin
中科院分区：
文献类型：
--
作者：
Damasevicius, Robertas;Napoli, Christian;Wozniak, Marcin
提出了一种基于经验模态分解（EMD）构造的内禀模态函数（IMFs）的尺度自适应重混和去混的噪声消除方法。该方法通过使用启发式算法执行IMF模式解混来解决EMD中的模式混合问题，该算法最小化了由一阶IMF部分和生成的二阶IMF子集之间的相关性。最后给出了用该方法分析含噪随机二值序列抖动的实例。与经典的第一个IMF丢弃方法相比，所提出的方法可以获得更好的去噪结果（使用相关性、峰对峰值和AR(4)模型的可预测性进行评估）。(C) 2017 Elsevier B.V.版权所有
We propose a novel noise cancellation method based on the scale-adaptive remixing and demixing of Intrinsic Mode Functions (IMFs) constructed using Empirical Mode Decomposition (EMD). The method addresses the problem of mode mixing in the EMD by performing IMF mode demixing using a heuristic algorithm that minimizes correlation between subsets of second order IMFs generated from partial sums of first order IMFs. An illustrative example using the proposed method for jitter analysis of a noisy random binary sequence is presented. The proposed approach allows achieving better denoising results (evaluated using correlation, Peak-to-Peak value and predictability with AR(4) model) than the classic first IMF discarding approach. (C) 2017 Elsevier B.V. All rights reserved.