Abstract
The direct fast iterative filtering (dFIF) is a novel time–frequency signal processing technique for non-stationary and non-linear signals, which can be considered the improved method of fast iterative filtering (FIF). Firstly, the equivalent impulse response is analyzed based on extensive numerical experiments to reveal the performances of dFIF. And then, fractional Gaussian noise (fGn) with different Hurst exponents is constructed to study the power spectrum density (PSD) and the frequency band distribution of the equivalent filter banks of the dFIF. The corresponding comparison analysis results are also illustrated, including empirical mode decomposition (EMD), variational mode decomposition (VMD) and db4-wavelet transform. Furthermore, the zero-crossings detection and estimate of empirical variance analysis of IMFs are performed, which indicates that dFIF does not follow a dyadic filter bank. Based on these findings, two application cases are presented in extracting time-varying oscillations and trends using dFIF, EMD and VMD. The results show that with appropriate parameter selection, dFIF performs better than EMD and VMD in terms of decomposition orthogonality, accuracy and anti-mode mixing.
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