Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition
GEORGIA INST OF TECH ATLANTA SCHOOL OF MATHEMATICS
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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.
- Numerical Mathematics