Open Access
Issue
JNWPU
Volume 42, Number 6, December 2024
Page(s) 1039 - 1046
DOI https://doi.org/10.1051/jnwpu/20244261039
Published online 03 February 2025
  1. WANG Qiang. Research on hydroacoustic target recognition method based on deep learning theory[D]. Xi'an: Northwestern Polytechnical University, 2018 (in Chinese) [Google Scholar]
  2. ZHANG Shaokang, WANG Chao, SUN Qindong. Classification technique for hydroacoustic target noise recognition based on multi-category feature fusion[J]. Journal of Northwestern Polytechnical University, 2020, 38(2): 366–376 [Article] (in Chinese) [CrossRef] [EDP Sciences] [Google Scholar]
  3. JIN Anqi, ZENG Xiangyang. A novel deep learning method for underwater target recognition based on res-dense convolutional neural network with attention mechanism[J]. Journal of Marine Science and Engineering, 2023, 11(1): 69 [Article] [CrossRef] [Google Scholar]
  4. MCINTYRE Duncan, LEE Waltfred, FROUIN-MOUY Héloïse, et al. Influence of propellers and operating conditions on underwater radiated noise from coastal ferry vessels[J]. Ocean Engineering, 2021, 232: 109075 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  5. REN Jiawei, XIE Yuan, ZHANG Xiaowei, et al. UALF: a learnable front-end for intelligent underwater acoustic classification system[J]. Ocean Engineering, 2021, 264: 112394 [Google Scholar]
  6. GAO Miao, SHI Guoyou. Ship collision avoidance anthropomorphic decision-making for structured learning based on AIS with Seq-CGAN[J]. Ocean Engineering, 2020, 217: 107922 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  7. DING Yuwei. Review on passive sonar target recognition[J]. Technical Acoustics, 2004, 4: 253–257 [Google Scholar]
  8. KAMAL Suraj, MOHAMMED K Shameer, PILLAI P R Saseendran, et al. Deep learning architectures for underwater target recognition[C]//Proceedings of 2013 Ocean Electronics, Kochi, India, 2013 [Google Scholar]
  9. FERGUSON L Eric, RAMKRISHNAN Rish, WILLIAMS B Stefan, et al. Convolutional neural networks for passive monitoring of a shallow water environment using a single sensor[C]//Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, USA, 2017 [Google Scholar]
  10. HU Gang, WANG Kejun, PENG Yuan, et al. Deep learning methods for underwater target feature extraction and recognition[J]. Computational Intelligence and Neuroscience, 2018, 2018: 1214301 [Google Scholar]
  11. LI Junhao, YANG Honghui. The underwater acoustic target timbre perception and recognition based on the auditory inspired deep convolutional neural network[J]. Applied Acoustics, 2021, 182: 108210 [Article] [CrossRef] [Google Scholar]
  12. WANG Shuguang, ZENG Xiangyang. Robust underwater noise targets classification using auditory inspired time-frequency analysis[J]. Applied Acoustics, 2014, 78: 68–76 [Article] [CrossRef] [Google Scholar]
  13. ZHENG Yunliang, GONG Qiyong, ZHANG Shufang. Time-frequency feature-based underwater target detection with deep neural network in shallow sea[J]. Journal of Physics: Conference Series, 2021, 1756: 012006 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  14. MUHAMMAD Irfan, ZHENG Jiangbin, ALI Shahid, et al. DeepShip: an underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification[J]. Expert Systems with Applications, 2021, 183: 115270 [Article] [CrossRef] [Google Scholar]
  15. FENG Sheng, ZHU Xiaoqian. A transformer-based deep learning network for underwater acoustic target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1505805 [Google Scholar]
  16. ZHU Yunan, WANG Biao, ZHANG Youwen, et al. Convolutional neural network based filter bank multicarrier system for underwater acoustic communications[J]. Applied Acoustics, 2021, 177: 107920 [Article] [CrossRef] [Google Scholar]
  17. WANG Biao, ZHANG Wei, ZHU Yunan, et al. An underwater acoustic target recognition method based on AMNet[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 1–5 [Google Scholar]
  18. MUHAMMAD Khishe. DRW-AE: a deep recurrent-wavelet autoencoder for underwater target recognition[J]. IEEE Journal of Oceanic Engineering, 2022, 47(4): 1083–1098 [CrossRef] [Google Scholar]
  19. SUN Qinggang, WANG Kejun. Underwater single-channel acoustic signal multitarget recognition using convolutional neural networks[J]. The Journal of the Acoustical Society of America, 2022, 151(3): 2245–2254 [NASA ADS] [CrossRef] [Google Scholar]
  20. ZHANG Wen, LIN Bin, YAN Yulin, et al. Multi-features fusion for underwater acoustic target recognition based on convolution recurrent neural networks[C]//2022 8th International Conference on Big Data and Information Analytics, Guiyang, China, 2022 [Google Scholar]
  21. BERGMANN Paul, FAUSER Michael, SATTLEGGER David, et al. Uninformed students: student-teacher anomaly detection with discriminative latent embeddings[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020 [Google Scholar]
  22. ZHANG Lanyue, WU Di, HAN Xue, et al. Feature extraction of underwater target signal using Mel frequency cepstrum coefficients based on acoustic vector sensor[J]. Journal of Sensors, 2016, 2016: 7864213 [Google Scholar]

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