Expendable Conductivity–Temperature–Depth-Assisted Fast Underwater Sound Speed Estimation by Convolutional Neural Network with Reduced Fully Connected Layers

Author:

Li Sijia1,Zhang Hao1,Lu Jiajun1,Wu Pengfei1,Huang Wei1ORCID

Affiliation:

1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

Abstract

Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimation methods cannot obtain real-time sound speed distribution or rely too much on sonar observation data, we propose an SSP estimation method based on a convolutional neural network with reduced fully connected layers (RFC-CNN) in this paper. This method utilizes neural networks to extract the complex nonlinear features of various types of data. With the help of the historical SSPs and shallow seawater sound speed and temperature data obtained by expendable conductivity–temperature–depth probes (XCTDs), a more accurate estimation of the regional sound speed distribution can be realized quickly. This approach can save the observation cost and significantly improve the real-time performance of SSP estimation.

Funder

Fundamental Research Funds for the Central Universities, Ocean University of China

Natural Science Foundation of Shandong Province

China Postdoctoral Science Foundation

Qingdao Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

MDPI AG

Reference28 articles.

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5. Huang, W., Gao, F., Wang, J., and Xu, T. (2023). A review on the construction of underwater sound speed fields. J. Harbin Eng. Univ., 44.

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