Hilbert-Huang Transform (HHT) is a data analysis technique that is particularly useful for analysing non-stationary and nonlinear signals. It decomposes a complex signal into its intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) and then provides a time-frequency analysis of these IMFs using the Hilbert spectrum.
When dealing with a complex signal, it's essential to understand the nature of the signal components and whether they contain both real and imaginary parts. In HHT, you typically apply the transform to the entire complex signal, including both real and imaginary parts, to capture its complete information.
Here are some considerations:
1. Complex vs. Real Signals: If your signal is inherently complex, such as in the case of complex-valued data from certain sensors or systems (e.g., complex-valued Doppler radar signals), you should use the entire complex signal when applying HHT. This will allow you to analyse both the amplitude (real part) and phase (imaginary part) variations in the signal.
2. Real Signals: If your signal is real and does not have an inherent complex structure, you may still apply HHT to the real part of the signal. In many cases, this can provide valuable insights into the time-frequency characteristics of the signal.
Refer to this documentation for more information:
