Comparison of different classification algorithms for underwater target discrimination

Classification of underwater targets from the acoustic backscattered signals is considered. Several different classification algorithms are tested and benchmarked not only for their performance but also to gain insight to the properties of the feature space. Results on a wideband 80-kHz acoustic backscattered data set collected for six different objects are presented in terms of the receiver operating characteristic (ROC) and robustness of the classifiers wrt reverberation.

I. Introduction

The problem of classifying underwater targets from the acoustic backscattered signals involves discrimination between targets and nontarget objects as well as the characterization of background clutter. Several factors that complicate this process include: nonrepeatability and variation of the target signature with aspect angles, range and grazing angle, competing natural and man-made clutter, highly variable and reverberant operating environment, and lack of any a priori knowledge about the shape and geometry of the nontargets.