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Neural-Network-Based Classification of Commercial Ships From Multi-Influence Passive Signatures

Monitoring the underwater environment is important for maritime security, marine conservation, and mine countermeasures. With developments in computation and artificial intelligence, it is increasingly important to measure and classify underwater ship signatures. In this work, we design an artificial neural network that classifies commercial ships based on their multi-influence signature. In total, 103 ship passages were included in the considered data set, with signatures recorded as the ship crossed a line of passive underwater sensors. The multi-influence signature was formed by feature-level sensor fusion of the hydroacoustic signature, the underwater electric potential, and the static and alternating magnetic signatures. Ships were classified according to size, or type, as broadcast on the AIS. With feature-level fusion, the neural network will optimize the relationship between different types of signatures, emphasizing features with greater predictive power. At the same time, weak features, even if not independently adequate for classification, can add information that improves accuracy further. The developed neural network achieved a classification accuracy of 87.4% when classifying according to size. With augmented data to balance the classes, 85.0% classification accuracy was achieved when classifying according to ship type. This is a large improvement on the found classification accuracy when using only hydroacoustic or electromagnetic signatures. This article verifies the value of feature-level sensor fusion in classification, and provides guidance on classifier design depending on the exact ship classification task.