ArticlePloS one2022
Underwater acoustic target recognition method based on a joint neural network.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An efficient deep learning approach with frequency and channel optimization for underwater acoustic target recognition.Scientific reports · 2025Article
- A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
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Abstract
To improve the recognition accuracy of underwater acoustic targets by artificial neural network, this study presents a new recognition method that integrates a one-dimensional convolutional neural network and a long short-term memory network. This new network framework is constructed and applied to underwater acoustic target recognition for the first time. Ship acoustic data are used as input to evaluate the network performance. A visual analysis of the recognition results is performed. The results show that this method can realize the recognition and classification of underwater acoustic targets. Compared with a single neural network, the relevant indices, such as the recognition accuracy of the joint network are considerably higher. This provides a new direction for the application of deep learning in the field of underwater acoustic target recognition.
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Registered trials
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