首页> 外国专利> TECHNIQUES FOR EMPIRICAL MODE DECOMPOSITION (EMD)-BASED SIGNAL DE-NOISING USING STATISTICAL PROPERTIES OF INTRINSIC MODE FUNCTIONS (IMFS)

TECHNIQUES FOR EMPIRICAL MODE DECOMPOSITION (EMD)-BASED SIGNAL DE-NOISING USING STATISTICAL PROPERTIES OF INTRINSIC MODE FUNCTIONS (IMFS)

机译:利用内在模式函数(IMFS)的统计特性的基于经验模态分解(EMD)的信号去噪技术

摘要

Techniques for EMD-based signal de-noising are disclosed that use statistical characteristics of IMFs to identify information-carrying IMFs for the purposes of partially reconstructing the identified relevant IMFs into a de-noised signal. The present disclosure has identified that the statistical characteristics of IMFs with noise tend to follow a generalized Gaussian distribution (GGD) versus only a Gaussian or Laplace distribution. Accordingly, a framework for relevant IMF selection is disclosed that includes, in part, performing a null hypothesis test against a distribution of each IMF derived from the use of a generalized probability density function (PDF). IMFs that contribute more noise than signal may thus be identified through the null hypothesis test. Conversely, the aspects and embodiments disclosed herein enable the determination of which IMFs have a contribution of more signal than noise. Thus, a signal may be partially reconstructed based on the predominately information-carrying IMFs to result in de-noised output signal.
机译:公开了用于基于EMD的信号去噪的技术,其使用IMF的统计特性来识别携带信息的IMF,以将识别出的相关IMF部分地重构为去噪的信号。本公开已经识别出,具有噪声的IMF的统计特性趋向于遵循广义的高斯分布(GGD),而不是仅遵循高斯或拉普拉斯分布。因此,公开了一种用于相关IMF选择的框架,该框架包括部分地针对由使用广义概率密度函数(PDF)得出的每个IMF的分布执行零假设检验。因此,通过零假设检验可以识别出比信号贡献更多噪声的IMF。相反,本文公开的方面和实施例使得能够确定哪些IMF具有比噪声更多的信号贡献。因此,可以基于主要携带信息的IMF来部分地重构信号,以产生去噪的输出信号。

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