An Attention-Assisted Multi-Modal Data Fusion Model for Real-Time Estimation of Underwater Sound Velocity

TL;DR

This paper introduces a novel attention-assisted multimodal neural network for real-time underwater sound velocity estimation, reducing reliance on on-site data collection and improving accuracy and robustness.

Contribution

The paper presents a self-attention embedded CNN model that fuses remote sensing and historical data for accurate, real-time underwater sound velocity profile estimation.

Findings

Lower RMSE compared to existing methods

Stronger robustness in diverse conditions

Effective fusion of remote sensing and historical data

Abstract

The estimation of underwater sound velocity distribution serves as a critical basis for facilitating effective underwater communication and precise positioning, given that variations in sound velocity influence the path of signal transmission. Conventional techniques for the direct measurement of sound velocity, as well as methods that involve the inversion of sound velocity utilizing acoustic field data, necessitate on--site data collection. This requirement not only places high demands on device deployment, but also presents challenges in achieving real-time estimation of sound velocity distribution. In order to construct a real-time sound velocity field and eliminate the need for underwater onsite data measurement operations, we propose a self-attention embedded multimodal data fusion convolutional neural network (SA-MDF-CNN) for real-time underwater sound speed profile (SSP) estimation. The proposed model seeks to elucidate the inherent relationship between remote sensing sea surface temperature (SST) data, the primary component characteristics of historical SSPs, and their spatial coordinates. This is achieved by employing CNNs and attention mechanisms to extract local and global correlations from the input data, respectively. The ultimate objective is to facilitate a rapid and precise estimation of sound velocity distribution within a specified task area. Experimental results show that the method proposed in this paper has lower root mean square error (RMSE) and stronger robustness than other state-of-the-art methods.