Adapting pre-trained deep learning models to new and unknown environments is a difficult challenge in underwater acoustic localization. We show that although pre-trained models have performance that suffers from mismatch between the training and test data, they generally exhibit a higher ``implied uncertainty'' in environments where there is more mismatch. Leveraging this notion of implied uncertainty, we partition the test samples into more certain and less certain sets, and implement an estimation method using the certain samples to improve the labeling for uncertain samples, which helps to adapt the model. We use an efficient method to quantify model prediction uncertainty, and an innovative approach to adapt a pre-trained model to unseen underwater environments at test time. This eliminates the need for labeled data from the target environment or the original training data. This adaptation is enhanced by integrating an independent estimate based on the received signal energy. We validate the approach extensively using real experimental data, as well as synthetic data consisting of model-generated signals with real ocean noise. The results demonstrate significant improvements in model prediction accuracy, underscoring the potential of the method to enhance underwater acoustic localization in diverse, noisy, and unknown environments.

翻译：将预训练的深度学习模型适配到新的未知环境是水声定位中的一个难题。研究表明，尽管预训练模型在训练数据与测试数据失配时性能会下降，但它们在失配程度更高的环境中通常表现出更高的"隐含不确定性"。利用这种隐含不确定性的概念，我们将测试样本划分为高确定性集和低确定性集，并利用确定性样本实施一种估计方法来改进对不确定性样本的标注，从而辅助模型自适应。我们采用高效的方法量化模型预测不确定性，并通过创新方法在测试时将预训练模型适配到未见过的水下环境。这种方法无需目标环境的标注数据或原始训练数据。通过整合基于接收信号能量的独立估计，进一步增强了自适应效果。我们使用真实实验数据以及由模型生成信号与真实海洋噪声构成的合成数据进行了广泛验证。结果表明该方法能显著提升模型预测精度，凸显了其在多样化、高噪声和未知环境中增强水声定位能力的潜力。