经验模态分解的抽样极限

Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005. Pub Date : 2005-08-28 DOI:10.1109/ISSPA.2005.1581021

N. Stevenson, M. Mesbah, B. Boashash

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引用次数: 44

摘要

本文的目的是研究采样对经验模态分解(EMD)的影响。为此，利用线性调频(LFM)信号的实验来模拟不同的采样率。实验表明，当信号的频率含量(fc)接近采样频率(fs)时，由于幅值分辨率较差，EMD表现不佳。这导致了采样限制的定义，即5倍于Nyqvist速率(fs/10)，以提高EMD的性能。在此采样极限下，对模拟信号和真实信号进行了对比仿真。结果表明，本征模态函数(IMF)正交性、IMF能量分布和IMF相干性均有显著改善。

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A sampling limit for the empirical mode decomposition

The aim of this paper is to investigate the effect of sampling on the empirical mode decomposition (EMD). To this end, an experiment utilising linear frequency modulated (LFM) signals was used to simulate different sampling rates. This experiment showed that as the frequency content of the signal (fc) approached the sampling frequency (fs) the EMD performed poorly due to poor amplitude resolution. This led to a definition of a sampling limit that was 5 times the Nyqvist rate (fs/10) to improve the performance of the EMD. Comparative simulation with this sampling limit was conducted on a simulated and a real world signal. The results exhibited significant improvement in intrinsic mode function (IMF) orthogonality, the distribution of IMF energy and IMF coherence.

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Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005.

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