Detrending and denoising with empirical mode decompositions
Detrending and denoising with empirical mode decompositions
复制标题
DOI：
--
复制
发表时间：
2004-09
期刊：
影响因子：
--
通讯作者：
P. Flandrin;P. Gonçalves;G. Rilling
中科院分区：
文献类型：
--
作者：
P. Flandrin;P. Gonçalves;G. Rilling
经验模式分解（EMD）最近被引入作为一个本地和完全的数据驱动的技术，旨在分解非平稳多分量信号的“内在”AM-FM的贡献。虽然经验模态分解原理很吸引人，而且易于实现，但由于没有可用的分析描述，性能分析是困难的。我们将在这里报告的数值模拟说明的潜力和局限性，EMD在两个信号处理任务，即去趋势和去噪。在这两种情况下，想法是利用部分重建，相关的模式被选择的基础上的统计特性的模式，已经验建立。
Empirical Mode Decomposition (EMD) has recently been introduced as a local and fully data-driven technique aimed at decomposing nonstationary multicomponent signals in “intrinsic” AM-FM contributions. Although the EMD principle is appealing and its implementation easy, performance analysis is difficult since no analytical description of the method is available. We will here report on numerical simulations illustrating the potentialities and limitations of EMD in two signal processing tasks, namely detrending and denoising. In both cases, the idea is to make use of partial reconstructions, the relevant modes being selected on the basis of the statistical properties of modes that have been empirically established.