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Shock and Vibration
Volume 2017, Article ID 6930605, 9 pages
https://doi.org/10.1155/2017/6930605
Research Article

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

1Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen 361005, China
2Science and Technology on Underwater Acoustic Antagonizing Laboratory, Systems Engineering Research Institute of China State Shipbuilding Corporation, Beijing 100036, China

Correspondence should be addressed to En Cheng; nc.ude.umx@negnehc

Received 14 December 2016; Revised 5 April 2017; Accepted 27 April 2017; Published 17 May 2017

Academic Editor: Carlo Trigona

Copyright © 2017 Jiaquan Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. J. Malinowski and I. Gloza, “Underwater noise characteristics of small ships,” Acta Acustica United with Acustica, vol. 88, no. 5, pp. 718–721, 2002. View at Google Scholar · View at Scopus
  2. De Seixas J. M. and N. N. De Moura, “Preprocessing passive sonar signals for neural classification, IET Radar,” IET Radar, Sonar & Navigation, vol. 5, no. 6, pp. 605–612, 2011. View at Google Scholar
  3. S. E. Crocker, P. L. Nielsen, J. H. Miller, and M. Siderius, “Geoacoustic inversion of ship radiated noise in shallow water using data from a single hydrophone,” Journal of the Acoustical Society of America, vol. 136, no. 5, pp. EL362–EL368, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Zheng, H. Yu, X. Chen, and C. Sun, “Line spectrum detection algorithm based on the phase feature of target radiated noise,” Journal of Systems Engineering and Electronics, vol. 27, no. 1, pp. 72–80, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Zeng and S. Wang, “Bark-wavelet analysis and hilbertehuang transform for underwater target recognition,” Defence Technology, vol. 9, no. 2, pp. 115–120, 2013. View at Publisher · View at Google Scholar
  6. Q. Li, J. Wang, and W. Wei, “An application of expert system in recognition of radiated noise of underwater target,” in Proceedings of the MTS/IEEE Oceans’ 95, pp. 404–408, San Diego, CA, USA, 2013. View at Publisher · View at Google Scholar
  7. C. Kang, X. Zhang, A. Zhang, and H. Lin, “Underwater acoustic targets classification using welch spectrum estimation and neural networks,” Advances in Neural Networks, pp. 930–935, 2004. View at Google Scholar
  8. R. H. Baran and J. P. Coughlin, “A neural network for target classification using passive sonar,” in Proceedings of the Conference on Analysis of Neural Network Applications, pp. 188–198, 1991.
  9. I. P. Kirsteins, S. K. Mehta, and J. Fay, “Adaptive separation of unknown narrowband and broadband time series,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2525–2528, Seattle, Wash, USA, 1998. View at Scopus
  10. J. Ribeiro-Fonseca and L. Correia, “Identification of underwater acoustic noise,” in Proceedings of Oceans’ 94, pp. 597–602, Brest, France. View at Publisher · View at Google Scholar
  11. Q. Wang, “Underwater bottom still mine classification using robust time-frequency feature and relevance vector machine,” International Journal of Computer Mathematics, vol. 86, no. 5, pp. 794–806, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Averbuch, V. Zheludev, P. Neittaanmäki, P. Wartiainen, K. Huoman, and K. Janson, “Acoustic detection and classification of river boats,” Applied Acoustics, vol. 72, no. 1, pp. 22–34, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Yao, M. R. Azimi-Sadjadi, A. A. Jamshidi, and G. J. Dobeck, “A study of effects of sonar bandwidth for underwater target classification,” IEEE Journal of Oceanic Engineering, vol. 27, no. 3, pp. 619–627, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. M. R. Azimi-Sadjadi, D. Yao, Q. Huang, and G. J. Dobeck, “Underwater target classification using wavelet packets and neural networks,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 784–794, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Wang and X. Zeng, “Robust underwater noise targets classification using auditory inspired time-frequency analysis,” Applied Acoustics, vol. 78, pp. 68–76, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. O. Adam, “The use of the Hilbert-Huang transform to analyze transient signals emitted by sperm whales,” Applied Acoustics, vol. 67, no. 11, pp. 1134–1143, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Li, L. Xie, and Y. Qin, “Underwater target feature extraction using Hilbert-Huang transform,” Journal of Harbin Enginerring University, vol. 30, no. 5, pp. 542–546, 2010. View at Google Scholar
  18. J. Shi and M. Liang, “Intelligent bearing fault signature extraction via iterative oscillatory behavior based signal decomposition (IOBSD),” Expert Systems with Applications, vol. 45, pp. 40–55, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. I. W. Selesnick, “Resonance-based signal decomposition: a new sparsity-enabled signal analysis method,” Signal Processing, vol. 91, no. 12, pp. 2793–2809, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. C.-T. Tan, I. W. Selesnick, and K. Avci, “Perceived quality of resonance based decomposed speech components under diotic and dichotic listening,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 925–929, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. I. W. Selesnick, “Wavelet transform with tunable Q-factor,” IEEE Transactions on Signal Processing, vol. 59, no. 8, pp. 3560–3575, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. M. Elad, J.-L. Starck, P. Querre, and D. L. Donoho, “Simultaneous cartoon and texture image inpainting using morphological component analysis MCA,” Applied and Computational Harmonic Analysis, vol. 19, no. 3, pp. 340–358, 2005. View at Publisher · View at Google Scholar · View at MathSciNet
  23. J. L. Starck, Y. Moudden, J. Bobina, M. Elad, and D. L. Donoho, “Morphological component analysis,” in Proceedings of Optics & Photonics, International Society for Optics and Photonics, 2005.
  24. Y. Zhao, Z. Li, and Y. Nie, “A time-frequency analysis method for low frequency oscillation signals using resonance-based sparse signal decomposition and a frequency slicewavelet transform,” Energies, vol. 9, no. 3, article 151, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. D. Zhang, D. Yu, and W. Zhang, “Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes,” Measurement Science and Technology, vol. 26, no. 11, p. 115003, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. S. T. N. Nguyen and W. A. Al-Ashwal, “Sea Clutter Mitigation Using Resonance-Based Signal Decomposition,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 11, pp. 2257–2261, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. N. E. Huang and Z. Wu, “A review on Hilbert-Huang transform: method and its applications to geophysical studies,” Reviews of Geophysics, vol. 46, no. 2, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. L. G. N. Martins, M. B. Stefanello, G. A. Degrazia et al., “Employing the Hilbert–Huang Transform to analyze observed natural complex signals: calm wind meandering cases,” Physica A: Statistical Mechanics and its Applications, vol. 462, pp. 1189–1196, 2016. View at Publisher · View at Google Scholar · View at Scopus
  29. K. Fu, J. Qu, Y. Chai, and T. Zou, “Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals,” Biomedical Signal Processing and Control, vol. 18, pp. 179–185, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. V. Vapnik, The Nature of Statistical Learning Theory, Springer Science & Business Media, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  31. V. N. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988–999, 1999. View at Publisher · View at Google Scholar