Hilbert–Huang transform

<p>The Hilbert–Huang transform (HHT) is a way to decompose a <a href="/facts/Signal_processing/Npaja6zb">signal</a> into so-called intrinsic mode functions (IMF) along with a trend, and obtain <a href="/facts/Instantaneous_frequency/fJ17zuan">instantaneous frequency</a> data.  It is designed to work well for data that is <a href="/facts/Stationary_process/vozdgfEX">nonstationary</a> and <a href="/facts/Nonlinear/4aYItALC">nonlinear</a>. 
</p><p>The Hilbert–Huang transform (HHT), a <a href="/facts/NASA/TIMCV0yV">NASA</a> designated name, was proposed by <a href="/facts/Norden_E._Huang/TtNzAnMv">Norden E. Huang</a>. It is the result of the empirical mode decomposition (EMD) and the <a href="/facts/Hilbert_spectral_analysis/DLFRarmF">Hilbert spectral analysis</a> (HSA). The HHT uses the EMD method to decompose a <a href="/facts/Signal_processing/Npaja6zb">signal</a> into so-called intrinsic mode functions (IMF) with a trend, and applies the HSA method to the IMFs to obtain <a href="/facts/Instantaneous_frequency/fJ17zuan">instantaneous frequency</a> data. Since the signal is decomposed in time domain and the length of the IMFs is the same as the original signal, HHT preserves the characteristics of the varying frequency. This is an important advantage of HHT since a real-world signal usually has multiple causes happening in different time intervals. The HHT provides a new method of analyzing <a href="/facts/Stationary_process/vozdgfEX">nonstationary</a> and <a href="/facts/Nonlinear/4aYItALC">nonlinear</a> time series data.
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Hilbert–Huang transform open-in-new

Hilbert–Huang transform