A methodology for detection and classification of some underwater acoustic signals using time-frequency analysis techniques

Abstract

Signal detection techniques based on time-frequency signal analysis with the Wigner-Ville distribution (WVD) and the cross Wigner-Ville distribution (XWVD) are presented. These techniques are shown to provide high-resolution signal characterization in a time-frequency space, and good noise rejection performance. This type of detection is applied to the signaturing, detection, and classification of specific machine sounds: the individual cylinder firings of a marine engine. For this task, a four-step procedure has been devised. The autocorrelation function (ACF) is first used to ascertain the number of engine cylinders and the firing rate of the engine. Further correlation techniques are then used to detect the time at which individual cylinder firing events occur. WVD- and XWVD-based analyses follow to procedure high-resolution time-frequency signatures. Finally, two-dimensional correlations are used for the classification of the individual cylinders. The proposed methodology is tested on real data. XWVD-based detection is also applied to detection of a transient with unknown waveshape (using real data).