Empirical Mode Decomposition: Theory and Applications in Underwater Acoustics

Abstract

Empirical mode decomposition (EMD) is a signal processing method that produces a data-driven multiresolution representation in time-domain suited to characterize time-varying and nonlinear phenomena. In EMD, intrinsic mode functions (IMF) are sequentially estimated from the signal of interest to represent different intrinsic oscillation modes and produce an orthogonal representation of the original information. Different algorithms have been proposed for IMF estimation to deal with limitations such as mode-mixing and noise sensitivity. EMD is usually associated with the Hilbert transform to obtain a frequency-domain representation. In this case, the method is referred to as the Hilbert-Huang transform (HHT). This paper presents a theoretical review of the fundamental aspects of both EMD and HHT, such as the IMF estimation procedure. Variations of the original EMD algorithm are also presented. Both simulated and experimental underwater acoustic signals are used to illustrate the efficiency of EMD/HHT in revealing relevant characteristics from time-varying and nonlinear phenomena.