Abstract
Sound speed profiles (SSPs) are essential parameters underwater that affects the propagation mode of underwater signals and has a critical impact on the energy efficiency of underwater acoustic communication and accuracy of underwater acoustic positioning. Traditionally, SSPs can be obtained by matching field processing (MFP), compressive sensing (CS), and deep learning (DL) methods. However, existing methods mainly rely on on-site underwater sonar observation data, which put forward strict requirements on the deployment of sonar observation systems. To achieve high-precision estimation of sound velocity distribution in a given sea area without on-site underwater data measurement, we propose a multi-modal data-fusion generative adversarial network model with residual attention block (MDF-RAGAN) for SSP construction. To improve the model's ability for capturing global spatial feature correlations, we embedded the attention mechanisms, and use residual modules for deeply capturing small disturbances in the deep ocean sound velocity distribution caused by changes of SST. Experimental results on real open dataset show that the proposed model outperforms other state-of-the-art methods, which achieves an accuracy with an error of less than 0.3m/s. Specifically, MDF-RAGAN not only outperforms convolutional neural network (CNN) and spatial interpolation (SITP) by nearly a factor of two, but also achieves about 65.8\% root mean square error (RMSE) reduction compared to mean profile, which fully reflects the enhancement of overall profile matching by multi-source fusion and cross-modal attention.
Abstract (translated)
声速剖面（SSPs）是水下环境中的关键参数，它影响着水下信号的传播模式，并对水下声学通信的能量效率和水下声定位的准确性有着至关重要的影响。传统上，可以通过现场处理匹配（MFP）、压缩感知（CS）以及深度学习（DL）方法来获取SSPs。然而，现有的大多数方法主要依赖于现场水下声呐观测数据，这对声呐观测系统的部署提出了严格的要求。为了在不进行现场测量的情况下，在给定海域内实现高精度的声速分布估计，我们提出了一种多模态数据融合对抗生成网络模型（MDF-RAGAN），该模型采用残差注意力模块构建SSP。为增强模型捕捉全局空间特征相关性的能力，我们在模型中嵌入了注意机制，并使用残差模块来深入捕获由于海表温度变化而引起的深海水声速分布的微小扰动。 在真实开放数据集上的实验结果表明，所提出的模型优于其他最先进的方法，在误差小于0.3米/秒的情况下达到了更高的精度。具体而言，MDF-RAGAN不仅比卷积神经网络（CNN）和空间插值法（SITP）高出近两倍的表现，还与平均剖面相比减少了约65.8%的均方根误差（RMSE），这充分反映了多源融合及跨模态注意力对整体剖面对齐增强的效果。
URL
https://arxiv.org/abs/2507.11812
