Fused Classification of Surface Ships Based on Hydroacoustic and Electromagnetic Signatures

Abstract

This paper reports a classification study using data-fusion on real-world, underwater signatures from surface ships. In this study we design and evaluate classifiers optimized to discriminate between small and big ships based on features extracted from extremely low frequency electric (ELFE) and hydroacoustic signatures. Classification is performed separately on each signature, and finally the individual decisions are fused into a common decision. The data set analysed here was recorded at a sea trial conducted in the Baltic Sea off the east coast of Sweden. The data set contains 23 passages of surface ships of various size, divided into small (up to 21484 tons) and big (above 21484 tons)ships based on their displacement.