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A study of Hilbert-Huang transform and its filtering character

Yang Liu

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Abstract

Hilbert-Huang Transform(HHT) is a new two-step time-frequency analytic method to analyze the non-linear and non-stationary signal.The key step of this method is empirical mode decomposition(EMD) method with which any complicated data set can be decomposed into several Intrinsic Mode Function(IMF) components.Using Hilbert transform to those IMF components can yield instantaneous frequency.The empirical mode decomposition(EMD) can be interpreted as a temporal and spatial filtering based on the signal's extremum characteristic scale.This method preserves the nonlinearity and non-stability of signal,and has potential superiority in filtering and de-noising. The experimental results clarify the advance and efficient of this method.

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What this paper is about

Hilbert-Huang Transform(HHT) is a new two-step time-frequency analytic method to analyze the non-linear and non-stationary signal.The key step of this method is empirical mode decomposition(EMD) method with which any complicated data set can be decomposed into several Intrinsic Mode Function(IMF) components.Using Hilbert transform to those IMF components can yield instantaneous frequency.The empirical mode decomposition(EMD) can be interpreted as a temporal and spatial filtering based on the signal's extremum characteristic scale.This method preserves the nonlinearity and non-stability of signal,and has potential superiority in filtering and de-noising. The experimental results clarify the advance and efficient of this method.

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Available abstract

Hilbert-Huang Transform(HHT) is a new two-step time-frequency analytic method to analyze the non-linear and non-stationary signal.The key step of this method is empirical mode decomposition(EMD) method with which any complicated data set can be decomposed into several Intrinsic Mode Function(IMF) components.Using Hilbert transform to those IMF components can yield instantaneous frequency.The empirical mode decomposition(EMD) can be interpreted as a temporal and spatial filtering based on the signal's extremum characteristic scale.This method preserves the nonlinearity and non-stability of signal,and has potential superiority in filtering and de-noising. The experimental results clarify the advance and efficient of this method.

Key concepts: Hilbert–Huang transform, Hilbert transform, Instantaneous phase, Hilbert spectral analysis, SIGNAL (programming language), Mode (computer interface), Algorithm, Analytic signal

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