The classification of underwater acoustic target signals based on wave structure and support vector machine

Abstract

The sound of propeller is a remarkable feature of ship-radiated noise, the loudness and timbre of which are usually applied to identify types of ships. Since the information of loudness and timbre is indicated in the wave structure of time series, the feature of wave structure can be extracted to classify types of various underwater acoustic targets. In this paper, the method of feature vector extraction of underwater acoustic signals based on wave structure is studied. The nine-dimension features are constructed via signal statistical characteristics of zero-crossing wavelength, peek-to-peek amplitude, zero-crossing wavelength difference, and wave train areas. And then, the support vector machine (SVM) is applied as a classifier for two kinds of underwater acoustic target signals. The kernel function is set radial basis function (RBF). By properly setting the penalty factor and parameter of RBF, the recognition rate reaches over 89.5%, respectively. The sea-test data shows the validity of target recognition ability of the method above.