Low-Complexity Sinusoidal-Assisted EMD (SAEMD) Algorithms for Solving Mode-Mixing Problems in HHT
Journal
Digital Signal Processing(DSP)
Journal Volume
24
Pages
45-69
Date Issued
2014
Author(s)
Abstract
In recent years, Hilbert–Huang Transform (HHT) is widely used to analyze nonlinear and non-stationary signals in various applications, such as seismic and biomedical signal processing. In HHT, the Empirical Mode Decomposition (EMD) is the key component for decomposing natural signals into intrinsic mode functions (IMFs). Since the EMD suffers from mode-mixing problem, in which some fast intermittent signals riding on a slow-oscillating wave, the Ensemble-EMD (EEMD) is proposed to solve this problem with the aids of noise. However, the EEMD requires high computational complexity in ensemble and is unsuitable for some real-time applications, such as ultrasound systems. In this paper, intermittent signals are modeled in mathematical forms for IMF decomposition. We then propose sinusoidal-assisted EMD (SAEMD) for efficient and effective HHT computation to solve mode-mixing problems. The type I of SAEMD (SAEMD-I) is initially proposed to solve the mode-mixing problem with very low computational complexity. However, if the maximum frequency of data is unknown in some real-world applications, the SAEMD-I may encounter estimation error caused by imprecise locations of extrema. For practical data, the type II of SAEMD (SAEMD-II) is proposed to solve the sampling rate issue. Compared with the ensemble-100 EEMD, the SAEMD-II can have 11–13 times improvement in terms of computation speed in El Ni?o application and comparable correlation coefficient (?0.95 at IMF 8). Hence, the proposed SAEMD-II scheme is a good candidate of implementing cost-effective HHT when computational complexity and real-time data processing are of major concern. ? 2013 Elsevier Inc.
SDGs
Other Subjects
Computational complexity; Cost effectiveness; Data handling; Frequency estimation; Mathematical transformations; Mixing; Problem solving; Real time systems; Correlation coefficient; Empirical Mode Decomposition; Intrinsic Mode functions; Low computational complexity; Mode mixing; Nonlinear and non-stationary signals; Real-time data processing; SAEMD; Signal processing
Type
journal article