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Open AccessReview
Artificial Intelligence-Based Underwater Acoustic Target Recognition: A Survey
by
Sheng Feng
Sheng Feng 1
,
Shuqing Ma
Shuqing Ma 2,
Xiaoqian Zhu
Xiaoqian Zhu 2,* and
Ming Yan
Ming Yan 2
1
College of Computer Science, National University of Defense Technology, Changsha 410073, China
2
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(17), 3333; https://doi.org/10.3390/rs16173333 (registering DOI)
Submission received: 7 August 2024
/
Revised: 5 September 2024
/
Accepted: 6 September 2024
/
Published: 8 September 2024
Abstract
Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing.
Share and Cite
MDPI and ACS Style
Feng, S.; Ma, S.; Zhu, X.; Yan, M.
Artificial Intelligence-Based Underwater Acoustic Target Recognition: A Survey. Remote Sens. 2024, 16, 3333.
https://doi.org/10.3390/rs16173333
AMA Style
Feng S, Ma S, Zhu X, Yan M.
Artificial Intelligence-Based Underwater Acoustic Target Recognition: A Survey. Remote Sensing. 2024; 16(17):3333.
https://doi.org/10.3390/rs16173333
Chicago/Turabian Style
Feng, Sheng, Shuqing Ma, Xiaoqian Zhu, and Ming Yan.
2024. "Artificial Intelligence-Based Underwater Acoustic Target Recognition: A Survey" Remote Sensing 16, no. 17: 3333.
https://doi.org/10.3390/rs16173333
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