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.

中文翻译：

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基于多影响度被动签名的基于神经网络的商业船舶分类

监测水下环境对于海上安全，海洋保护和防雷措施非常重要。随着计算和人工智能的发展，测量和分类水下船舶签名变得越来越重要。在这项工作中，我们设计了一个人工神经网络，可以根据其多影响特征对商业船进行分类。在考虑的数据集中总共包含103条船舶通道，并在船舶越过被动水下传感器线时记录了签名。多影响签名是通过水声签名，水下电势以及静态和交变磁性签名的特征级传感器融合形成的。根据在AIS上广播的大小或类型对船舶进行分类。通过功能级融合，神经网络将优化不同类型签名之间的关系，从而以更大的预测能力强调特征。同时，即使不是独立地适合分类的弱项，也可以添加进一步提高准确性的信息。根据大小进行分类时，发达的神经网络实现了87.4％的分类精度。利用增强的数据来平衡类别，当按船型进行分类时，分类精度达到了85.0％。仅使用水声或电磁签名时，这对找到的分类精度有很大的改进。本文验证了特征级传感器融合在分类中的价值，并根据确切的船舶分类任务为分类器设计提供了指导。