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
Reference28 articles.
1. Fast and Accurate Underwater Acoustic Horizontal Ranging Algorithm for an Arbitrary Sound-Speed Profile in the Deep Sea;Tongwei;IEEE Internet Things J.,2022
2. Ahmed, A., and Younis, M. (2017, January 21–25). Distributed real-time sound speed profiling in underwater environments. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France.
3. Hu, J., Xiao, Y., Zhang, D., and Leng, L. (2019). Inversion prediction method for sound speed profile. Adv. Mar. Sci., 37.
4. Machine learning approach to prediction of real-time ocean sound speed profile;Madiligama;J. Acoust. Soc. Am.,2022
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.