Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition

Abstract

The empirical mode decomposition (EMD) is an algorithm pioneered by N. Huang et. al. as an alternative technique to the traditional Fourier and wavelet methods for analyzing nonlinear and non-stationary signals. It aims at decomposing a signal, via an iterative sifting procedure, into several intrinsic mode functions (IMFs), and each of the IMFs has better behaved instantaneous frequency analysis. This paper presents an alternative approach for EMD. The main idea is to replace the average of upper and lower envelopes in the sifting procedure of EMD by a local average obtained by variational optimization framework. Therefore, an IMF can be produced by simply subtracting the average from the signal without iteration. Our numerical examples illustrate that the resulting decomposition is convergent and robust against noise.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2014
Accession Number
ADA610276

Entities

People

  • Feng Zhou
  • Hao-min Zhou
  • Lihua Yang
  • Lijun Yang

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Compressed Sensing
  • Computational Complexity
  • Computational Science
  • Data Analysis
  • Decomposition
  • Fluid Mechanics
  • Frequency
  • Image Processing
  • Iterations
  • Mathematical Analysis
  • Mathematics
  • Molecular Dynamics
  • Optimization
  • Signal Processing
  • Stationary
  • Time Series Analysis

Fields of Study

  • Engineering

Readers

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  • Approximation Theory.
  • Life Cycle Cost Analysis