Variational Mode Decomposition for Cpp using Eigen.
This is cpp realization for Variatioanl Mode Decomposition
Coding by: Lang He ([email protected])
Refering to the paper: K. Dragomiretskiy, D. Zosso, Variational Mode Decomposition, IEEE Trans. on Signal Processing (in press)
u - the collection of decomposed modes (2D double Matrix in Eigen -MatrixXd)
u_hat - spectra of the modes (2D complex Matrix in Eigen -MatrixXd)
omega - estimated mode center - frequencies (2D double Matrix in Eigen -MatrixXd)
signal - the time domain signal(1D vector) to be decomposed
alpha - the balancing parameter of the data - fidelity constraint
tau - time - step of the dual ascent(pick 0 for noise - slack)
K - the number of modes to be recovered
DC - true if the first mode is putand kept at DC(0 - freq)
init - 0 = all omegas start at 0 1 = all omegas start uniformly distributed 2 = all omegas initialized randomly
tol - tolerance of convergence criterion; typically around 1e-6