Open Access
Issue
JNWPU
Volume 38, Number 2, April 2020
Page(s) 366 - 376
DOI https://doi.org/10.1051/jnwpu/20203820366
Published online 17 July 2020
  1. Wang Yinlin. Research on the Application of Hilbert-Huang Transformation to Vector Signal Processing[D]. Harbin: Harbin Engineering University, 2006(in Chinese) [Google Scholar]
  2. Li Xiukun, Xie Lei, Qin Yu. Underwater Target Feature Extraction Using Hibert-Huang Transform[J]. Journal of Harbin Engineering University, 2009, 30(5): 542–546 10.3969/j.issn.1006-7043.2009.05.014 (in Chinese) [Google Scholar]
  3. Li Xinxin. Research on Feature Extraction and Classification of Ship Noise and Whale Sound[D]. Harbin: Harbin Engineering University, 2012 (in Chinese) [Google Scholar]
  4. Han Xue. Feature Extraction of Underwater Target Based on Auditory Features[D]. Harbin: Harbin Engineering University, 2013(in Chinese) [Google Scholar]
  5. Shi Chaoxiong, Li Ganghu, He Huihui, et al. Application of the Lifting Wavelet Transform Based MFCC in Target Identification[J]. Technical Acoustics, 2014, 33(4): 372–375 [Article] (in Chinese) [Google Scholar]
  6. Cheng Yusheng, Wang Yichuan, Shi Guangzhi, et al. DEMON Analysis of Underwater Target Radiation Noise Based on Modern Signal Processing[J]. Technical Acoustics, 2006, 25(1): 276–281 [Article] (in Chinese) [Google Scholar]
  7. Ge Qing. The Information Fusion in Underwater Target Recognition[D]. Harbin: Harbin Engineering University, 2008(in Chinese) [Google Scholar]
  8. Wang Yang. Underwater Target Multidimensional Radiation Analysis of Characteristics of the Technology[D]. Harbin: Harbin Engineering University, 2012(in Chinese) [Google Scholar]
  9. Zhang Dawei, Zhang Xinhua, Fu Liufang, et al. Recognitiuon of Ships Based on Auditory Sense and Convolutional Neutral Methorks[J]. Technical Acoustics, 2015, 34(6): 181–184 [Article] (in Chinese) [Google Scholar]
  10. Lu Anan. Underwater Acoustic Classification Based on Deep Learning[D]. Harbin: Harbin Engineering University, 2017(in Chinese) [Google Scholar]
  11. Valdenegro-Toro M. Improving Sonar Image Patch Matching via Deep Learning[C]//2017 European Conference on Mobile Robots, Paris, 2017 [Google Scholar]
  12. Williams D P. Underwater Target Classification in Synthetic Aperture Sonar Imagery Using Deep Convolutional Neural Networks[C]//International Conference on Pattern Recognition 2016, Mexico, 2016: 2497–2502 [Google Scholar]
  13. Wang Qiang, Zeng Xiangyang. Deep Learning Methods and Their Applications in Underwater Targets in Recognization[J]. Technical Acoustics, 2015, 34(2): 138–140 [Article] (in Chinese) [Google Scholar]
  14. KAMAL S, MOHAMMED S K, PILLAI P R S, et al. Deep learning Architectures for Underwater Target Recognition[C]//Ocean Electronics, 2013: 48–54 [Google Scholar]
  15. Yang Honghui, Shen Sheng, Yao Xiaohui, et al. Underwater Acoustic Target Feature Learning and Recognition Using Hybrid Regularization Deep Belief Network[J]. Journal of Northwestern Polytechnical University, 2017, 35(2): 220–225 10.3969/j.issn.1000-2758.2017.02.008 (in Chinese) [Google Scholar]
  16. Chen Yuechao, Xu Xiaonan, Yao Xiaohui, et al. Underwater Target Recognition method based on Denoising Auto-Encoder[J]. Acoustics and Electronics Engineering, 2018, 1: 30–33 [Article] (in Chinese) [Google Scholar]
  17. Liu Tongming, Xia Zuxun, Xie Hongcheng. Technology and Application of Data Fusion Technicies[M]. Beijing: National Defense Industry Press, 1998 (in Chinese) [Google Scholar]
  18. Klaus Greff, Rupesh K, Srivastava, et al. LSTM:a Search Space Odyssey[J]. IEEE Trans on Neural Networks and Learning Systems, 2017, 28(10): 2222–2232 10.1109/TNNLS.2016.2582924 [CrossRef] [Google Scholar]
  19. Alex Graves. Learning Precise Timing with LSTM Recurrent Networks[J]. Journal of Machine Learning Research, 2002(3): 115–143 [Article] [Google Scholar]
  20. Wojciech Zaremba. Recurrent Neural Network Regularization[J/OL]. (2015-12-19)[2019-03-01]. https://arxir.org.pdf/1409.2329v5.pdf [Google Scholar]
  21. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, et al. Dropout:a Simple Way to Prevent Neural Networks from Overfitting[J]. Journal of Machine Learning Research, 2014, (15): 1929–1958 [Article] [Google Scholar]
  22. Xia Peilun. Target Tracking and Information Fusion[M]. Beijing: National Defense Industry Press, 2010 (in Chinese) [Google Scholar]
  23. Peter Flach. Machine Learning[M]. Beijing: Posts & Telecom Press, 2016 (in Chinese) [Google Scholar]
  24. Tian Tan, Liu Guozhi, Sun Dajun. Sonar Technology[M]. Harbin, Harbin Engineering University Press, 1999(in Chinese) [Google Scholar]
  25. Zhou Zhihua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016 (in Chinese) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.