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Journal of Marine Science and Engineering, 2023 Underwater acoustic target recognition is a hot research area in acoustic signal processing. With the development of deep learning, feature extraction and neural network computation have become two major steps of recognition. Due to the complexity of the
Ji Wu +5 more
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Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition. [PDF]
Sensors (Basel), 2018 Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples.
Yang H, Shen S, Yao X, Sheng M, Wang C.
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Xibei Gongye Daxue Xuebao, 2020 As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower
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A Survey of Underwater Acoustic Target Recognition Methods Based on Machine Learning
Journal of Marine Science and Engineering, 2023 Underwater acoustic target recognition (UATR) technology has been implemented widely in the fields of marine biodiversity detection, marine search and rescue, and seabed mapping, providing an essential basis for human marine economic and military ...
Xinwei Luo +3 more
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Frontiers in Marine Science, 2023 The recognition of underwater acoustic targets plays a crucial role in marine vessel monitoring. However, traditional underwater target recognition models suffer from limitations, including low recognition accuracy and slow prediction speed.
Zhe Chen +6 more
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Hybrid local-global representation learning with stochastic Gaussian classification for underwater acoustic target recognition [PDF]
Scientific ReportsUnderwater acoustic target recognition is critical for a broad spectrum of marine applications, yet its performance is often hindered by environmental variability, non-stationary propagation effects, and inherently low signal-to-noise ratio conditions ...
Cheng Yang +7 more
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A Lightweight Channel–Frequency Joint Attention Network for Underwater Ship-Radiated Noise Separation [PDF]
SensorsUnderwater acoustic signals (UAS) in marine environments are often contaminated by noise from biological activity, ocean waves, and equipment. Under low signal-to-noise ratio (SNR) conditions, target ship-radiated components and background noise are ...
Junxian Dong, Qiong Yang, Ying Li
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A lightweight hybrid attention network with multi-scale feature integration for intelligent recognition of underwater acoustic targets [PDF]
Scientific ReportsUnderwater Acoustic Target Recognition (UATR) plays a significant role in intelligent sonar and underwater environmental monitoring systems, but successful recognition in underwater environments of complex scenarios is a significant challenge because of ...
Nahid-Al Mahmud +8 more
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IEEE Access, 2021 Underwater acoustic target recognition is one of the main functions of the SONAR systems. In this paper, a target recognition method based on combined features with automatic coding and reconstruction is proposed to classify ship radiated noise signals ...
Xinwei Luo, Yulin Feng, Minghong Zhang
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Journal of Marine Science and EngineeringUnderwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks,
Kang Yang +3 more
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