Title :
Higher-order statistics and extreme waves
Author :
Powers, E.J. ; Park, I.-S. ; Im, S. ; Mehta, S. ; Yi, E.-J.
Author_Institution :
Center for Electron., Texas Univ., Austin, TX, USA
Abstract :
A sparse second-order time-domain Volterra model is used to decompose a random (sea) wave train into its first- and second-order components. Extreme waves are shown to result from short-term phase locking of the first- and second-order components. The feasibility of using a wavelet-based bicoherence “spectrum” to detect the strong, but short lived, phase coupling is investigated. The results are encouraging and suggest the wavelet-based bicoherence is a topic worth considering further
Keywords :
Volterra equations; geophysical signal processing; higher order statistics; ocean waves; random processes; spectral analysis; time-domain analysis; wavelet transforms; extreme waves; first-order components; higher-order statistics; phase coupling; random wave train decomposition; sea waves; second-order components; short-term phase locking; sparse second-order time-domain Volterra model; wavelet-based bicoherence spectrum; Frequency; Higher order statistics; Hurricanes; Marine vehicles; Petroleum; Phase detection; Probes; Production; Storms; Time domain analysis;
Conference_Titel :
Higher-Order Statistics, 1997., Proceedings of the IEEE Signal Processing Workshop on
Conference_Location :
Banff, Alta.
Print_ISBN :
0-8186-8005-9
DOI :
10.1109/HOST.1997.613495
