无需目标环境标签，用无监督方法提升水下声源定位模型泛化能力。
Joint Source-Environment Adaptation for Deep Learning-Based Underwater Acoustic Source Ranging
- 通过无监督域适应微调预训练模型参数
- 在真实海洋噪声下仍保持高定位精度
- 适合部署于未标注新水域的声学定位系统
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到新环境。采用无监督域适应技术，在不访问目标环境标签或原始训练数据的情况下，利用无监督损失函数微调模型参数。该方法通过耦合接收信号能量的独立估计（与声源相关）提升预训练模型的预测性能。实验在贝尔霍普生成的数据上进行，环境模拟了SWellEx-96实验场景，并叠加了来自KAM11实验的真实海洋噪声，验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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