In this paper, we propose a method to adapt a pre-trained deep-learning-based model for underwater acoustic localization to a new environment. We use unsupervised domain adaptation to improve the generalization performance of the model, i.e., using an unsupervised loss, fine-tune the pre-trained network parameters without access to any labels of the target environment or any data used to pre-train the model. This method improves the pre-trained model prediction by coupling that with an almost independent estimation based on the received signal energy (that depends on the source). We show the effectiveness of this approach on Bellhop generated data in an environment similar to that of the SWellEx-96 experiment contaminated with real ocean noise from the KAM11 experiment.

翻译：本文提出一种方法，将预训练的基于深度学习的水下声源定位模型适配至新环境。我们采用无监督域自适应技术提升模型的泛化性能，即通过无监督损失函数微调预训练网络参数，而无需目标环境的任何标签数据或模型预训练阶段使用的任何数据。该方法通过将预训练模型预测与基于接收信号能量（其依赖于声源）的近乎独立估计相结合，从而改进预训练模型的预测性能。我们在与SWellEx-96实验相似的环境中（使用Bellhop生成数据并混入KAM11实验的真实海洋噪声）验证了该方法的有效性。