The principle of EWT is to divide the Fourier spectrum of the signal into continuous intervals, then construct wavelet filter banks on each interval for filtering, and finally obtain a group of AM and FM components through signal reconstruction. This method can identify the position of the feature information in the Fourier spectrum of the signal by using the wavelet filter bank with tight support characteristics, and adaptively extract the different frequency components of the signal.

Installation: matlab

Operation mode:

Input variable: time series signal

Output variable: Constructed signal

QR code:

EMD processes non-stationary signals of time series, and decomposes the signals according to the time scale characteristics of the data itself, without setting any basis function in advance

Compared with EMD and EEMD, CEEMDAN can further eliminate mode aliasing.

Compared with EMD, EEMD can eliminate mode aliasing.

By solving the frequency domain variational optimization problem, each signal component is estimated. Assuming that all components are narrowband signals concentrated near their respective central frequencies, VMD establishes a constrained optimization problem according to the component narrowband conditions, so as to estimate the central frequencies of signal components and reconstruct the corresponding components.