Raised cosine filter-based empirical mode decomposition
Raised cosine filter-based empirical mode decomposition
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DOI：
10.1049/iet-spr.2009.0207
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发表时间：
2011-04
影响因子：
1.7
通讯作者：
A. Roy;J. Doherty
中科院分区：
文献类型：
--
作者：
A. Roy;J. Doherty
经验模式分解（EMD）是一种相对较新的多分量信号分解方法，它不需要关于分量的先验知识。在这项研究中，提出了一种改进的算法，使用升余弦插值，作者称之为升余弦经验模式分解。所提出的技术的分解质量是可控的，通过一个可调参数。这将导致更好的性能比原来的方法，产生更快的收敛速度或更低的最终误差在不同的条件下。一个有效的快速傅立叶变换为基础的实现所提出的技术。通过对多种多分量信号的分析，验证了新算法的信号分解性能，并与EMD算法进行了比较。比较了两种方法的计算复杂度。
The empirical mode decomposition (EMD) is a relatively new method to decompose multicomponent signals that requires no a priori knowledge about the components. In this study, a modified algorithm using raised cosine interpolation is proposed which the authors refer to as raised cosine empirical mode decomposition. The decomposition quality of this proposed technique is controllable via an adjustable parameter. This results in better performance than the original approach which produces faster convergence or lower final error under different conditions. An efficient fast Fourier transform-based implementation of the proposed technique is presented. The signal decomposition performance of the new algorithm is demonstrated by application to a variety of multicomponent signals and a comparison with EMD algorithm is presented. Computational complexity of the two techniques is compared.