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Interpretable features for underwater acoustic target recognition
Measurement: Journal of the International Measurement Confederation, 2021Abstract The major challenge of underwater acoustic target recognition is that the features clearly characterizing the underwater acoustic targets remain indistinct, where the sound signals are often submerged by intense noise. In this paper, we aim to discover an efficient interpretable feature set that can reveal the inherent mechanism, and result ...
Junjun Jiang +4 more
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Underwater Acoustic Target Recognition Using Software Defined Radio [PDF]
2023 46th International Conference on Telecommunications and Signal Processing (TSP), 2023 One of the important results of ship movement is acoustics noise which contains a lot of ship attributes. With developing technology, the classification of targets problem remains important. Recognition of ships is possible using ships' acoustic trace data.
İlhan, Hacı, Pehlivan, Adil Ugur
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Underwater acoustic target recognition on ShipsEar dataset
2023The task of classifying underwater audio source has various marine-oriented applications, including maritime and environmental monitoring, detection of marine life, and underwater surveillance. However, Underwater Acoustic Target Recognition (UATR) remains challenging due to several factors.
Tam Phi, David Han
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Unraveling complex data diversity in underwater acoustic target recognition through convolution-based mixture of experts [PDF]
Expert Systems With ApplicationsUnderwater acoustic target recognition is a difficult task owing to the intricate nature of underwater acoustic signals. The complex underwater environments, unpredictable transmission channels, and dynamic motion states greatly impact the real-world ...
Ji Xu, Yuan Xie, Jiawei Ren
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An Underwater Acoustic Target Recognition Method Based on Spectrograms with Different Resolutions [PDF]
Journal of Marine Science and Engineering, 2021 This paper focuses on the automatic target recognition (ATR) method based on ship-radiated noise and proposes an underwater acoustic target recognition (UATR) method based on ResNet.
Xinwei Luo
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Underwater acoustic targets recognition algorithm based on NMF
2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2020To solve the problem of low recognition rate of underwater targets for the reason of their large feature discreteness within class and high feature overlapping between classes, the underwater targets recognition algorithm based on NMF universal dictionary model (UDM) is proposed, in which, the UDM is established using the existing underwater acoustic ...
Xiaoqing Zheng +3 more
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Intelligent Recognition of Underwater Acoustic Target Noise on Underwater Glider Platform
2018 Chinese Automation Congress (CAC), 2018The underwater acoustic target detection system based on the underwater glider platform requires the platform itself to have the ability of target automatic tracking, identification and evaluation, but the traditional methods of underwater target noise identification have strong human-computer interaction characteristics.
Zhang Shao-Kang +3 more
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Underwater Acoustic Target Recognition Based on ReLU Gated Recurrent Unit
2020 6th International Conference on Robotics and Artificial Intelligence, 2020In general, the traditional acoustic models'(e. g. Gaussian mixture model, GMM) for underwater acoustic target recognition (UATR) performance in sequential data has so far been disappointing. In contrast, Recurrent Neural Network (RNN) is a powerful tool for sequential data.
Xiaodong Sun +5 more
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An Underwater Acoustic Target Recognition Method Based on Transfer Learning
2024 9th International Conference on Electronic Technology and Information Science (ICETIS)Underwater acoustic target recognition(UATR)is a challenging task Due to the high cost of sampling data, it is difficult to build a large-scale dataset.In this study,a new method based on transfer learning with a VGG16 model(Transfer-VGG16) is developed in which three-dimensional(3-D)data is used as the input.Use the dataset obtained in real scenarios
Xiaozhuo Yang +5 more
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