Abstract
The research on multi-target recognition of mixed underwater acoustic signals is of great significance for military missions, ocean development, and navigation safety assurance. Due to the limited availability of information and the significant impact of the seawater medium and marine environmental noise on mono-channel underwater acoustic signals, achieving reliable and accurate multi-target recognition remains challenging. To overcome the challenge of reliability in multi-target recognition of mixed underwater acoustic signals, this paper focuses on investigating a deep learning-based method for recognizing and classifying underwater acoustic signals. A recurrent neural network fused with deep convolutional networks is proposed for multi-target recognition of mixed underwater acoustic signals. The deep convolutional network, DenseNet, is employed to extract frequency domain features, and an attention mechanism is introduced to capture the most salient features. Finally, hybrid underwater acoustic signal recognition is achieved through a recurrent neural network LSTM. The analysis of experimental results demonstrates that the DenseNet-LSTM model can enhance the accuracy of mixed underwater acoustic signal recognition based on frequency domain features. Furthermore, by incorporating the attention mechanism, the recognition rate is further improved.
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Acknowledgments
This research received partial support from the R&D plan project in key fields of Guangdong Province (No. 2018B010109001, No. 2021B0707010001), as well as the key scientific research platform of universities in Guangdong Province (No. 2022KSYS016).
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Zhu, M., Zhang, X., Jiang, Y., Wang, K., Su, B., Wang, T. (2023). Hybrid Underwater Acoustic Signal Multi-Target Recognition Based on DenseNet-LSTM with Attention Mechanism. In: Deng, Z. (eds) Proceedings of 2023 Chinese Intelligent Automation Conference. CIAC 2023. Lecture Notes in Electrical Engineering, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-99-6187-0_72
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