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Feature Extraction in Fractional Fourier Domain for Classification of Passive Sonar Signals

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Abstract

The acoustic signals radiated from the marine vessels contain information about their machinery characteristics that can be useful for the detection and classification purposes. To achieve reliable accuracy in classification task, informative discriminant features should be extracted from received signals. STFT (Short Time Fourier Transform) is the most basic and popular signal processing method in the classification process of passive sonar signals, but it suffers from some fatal shortages. In this work, we present an improved spectrogram based on the windowed fractional-Fourier transform of the acoustic signal with the optimal FrFT order, which accounts for signals with multiple non-stationary components. The discriminating capability of two groups of features extracted from the processed signal, using PCA and LDA techniques, have been compared. The achieved results declare the significant improvement in the classification accuracy by the new proposed method using the LDA feature extraction technique and the proper order of FrFT. However, in order to eliminate the constraints of searching for the proper FrFT-order and increasing the reliability and stability of practical sonar classification systems, a parallel combination of the proposed method is introduced.

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Notes

  1. Linear Discriminant Analysis

  2. Principle Component Analyzer

References

  1. Li, Q., Wang, J., & Wei, W. (1995). An application of expert system in recognition of radiated noise of underwater target. Challenges Our Chang. Glob. Environ. Conf. Proceedings. Ocean. 95 MTS/IEEE, vol. 1.

  2. Soares-Filho, W., Manoel de Seixas, J., & Pereira Caloba, L. (2000). Averaging spectra to improve the classification of the noise radiated by ships using neural networks. In Proceedings. Sixth Brazilian Symposium on Neural Networks (vol. 1, pp. 156–161).

  3. Ward, M. K., & Stevenson, M. (2000). Sonar signal detection and classification using artificial neural networks. Canadian Conference on Electrical and Computer Engineering, 2, 717–721.

    Google Scholar 

  4. Atallah, L., & Probert Smith, P. J. (2003). Using wavelet analysis to classify and segment sonar signals scattered from underwater sea beds. International Journal of Remote Sensing, 24, 4113–4128.

    Article  Google Scholar 

  5. Seixas, J. M., Damazio, D. O., Diniz, P. S. R., & Soares-Fillho, W. (2001). Wavelet transform as a preprocessing method for neural classification of passive sonar signals. ICECS 2001. 8th IEEE Int. Conf. Electron Circuits Syst (vol. 1, pp. 83–86).

  6. Constable, R., & Thornhill, R. J. (1993). Using the discrete wavelet transform for time-frequency analysis of the surface EMG signal. In Biomedical Sciences Instrumentation (vol. 29, pp. 121–127).

  7. Baran, R. H., & Coughlin, J. P. (1991). A neural network for target classification using passive sonar. In Proceedings of the conference on Analysis of neural network applications - ANNA ‘91 (pp. 188–198).

  8. Soares-Filho, W., Seixas, J. M., & Caloba, L. P. (2002). Enlarging neural class detection capacity in passive sonar systems. 2002 I.E. Int. Symp. Circuits Syst. Proc. (vol. 3, pp. 105–108).

  9. Eom, K., Wellman, M., Srour, N., Hillis, D., & Chellappa, R. (1998). Acoustic target classification using multiscale methods. Defense Technical Information Center. https://doi.org/10.21236/ada358579

  10. Farrokhrooz, M., Karimi, M., & Rafiei, A. (2007). A new method for spread value estimation in multi-spread PNN and its application in ship noise classification. In 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Proceedings (pp. 16–20).

  11. Boashash, B. (1988). Note on the use of the Wigner distribution for time-frequency signal analysis. IEEE Transactions on Acoustics, 36, 1518–1521.

    Article  MATH  Google Scholar 

  12. Ghofrani, S., Ayatollahi, A., & Shamsollahi, M. B. (1993). Wigner – Ville distribution. Proceedings of the IEEE, 39, 2–2.

    Google Scholar 

  13. Ozaktas, H. M., Kutay, M. A., & Mendlovic, D. (1999). Introduction to the fractional fourier transform and its applications. Advances in Imaging and Electron Physics, 106, 239–291.

    Article  Google Scholar 

  14. McBRIDE, A. C., & Kerr, F. H. (1987). On Namias’s fractional fourier transforms. IMA Journal of Applied Mathematics, 39, 159–175.

    Article  MathSciNet  MATH  Google Scholar 

  15. Cohen, L. (1989). Time-frequency distributions-a review. Proceedings of the IEEE, 77(7), 941–981.

    Article  Google Scholar 

  16. Pragati, R., Vaibhav, M., & Rahul, P. (2013). Filtering in time-frequency domain using STFrFT. International Journal of Computers and Applications, 69, 975–8887.

    Google Scholar 

  17. Filho, W. S., de Seixas, J. M., & de Moura, N. N. (2011). Preprocessing passive sonar signals for neural classification. IET Radar, Sonar and Navigation, 5(6), 605.

    Article  Google Scholar 

  18. Ozaktas, H. M., & Mendlovic, D. (1995). Fractional fourier optics. Journal of the Optical Society of America. A, 12, 743–751.

    Article  Google Scholar 

  19. Ozaktas, H. M., Ankan, O., Alper Kutay, M., & Bozdagi, G. (1996). Digital computation of the fractional fourier transform. IEEE Transactions on Signal Processing, 44, 2141–2150.

    Article  Google Scholar 

  20. Fisher, R. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188.

    Article  Google Scholar 

  21. Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2, 559–572.

    Article  MATH  Google Scholar 

  22. Oza, N., & Tumer, K. (2001). Input decimation ensembles: decorrelation through dimensionality reduction. In Multiple Classifier Systems (pp. 238–247).

  23. Bishop, C. M. (2006). Pattern recognition and machine learning (Information Science and Statistics). New York: Springer-Verlag New York, Inc..

    MATH  Google Scholar 

  24. Peyvandi, H., Farrokhrooz, M., Roufarshbaf, H., Park, S.-J. (2011) SONAR systems and underwater signal processing: classic and modern approaches. In N.Z. Kolev (Ed.), Sonar Systems (pp. 173–206). Rijeka: InTech.

  25. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.

    Article  MathSciNet  MATH  Google Scholar 

  26. Breiman, L. (1999). Random forest. Machine Learning, 45(5), 1–35.

    Google Scholar 

  27. Chen, T., & Guestrin, C. (2016). XGBoost: reliable large-scale tree boosting system. arXiv, pp. 1–6.

  28. Lin, K. P., & Chen, M. S. (1991). On the design and analysis of the privacy-preserving SVM classifier. IEEE Transactions on Communications, 39(12), 1802–1812.

    Article  Google Scholar 

  29. McHugh, M. L. (2012). Interrater reliability: the Kappa statistic. Biochemia Medica, 22(3), 276–282.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, School of Engineering, Persian Gulf University, Bushehr, 75168, Iran

    Mehdi Shadlou Jahromi, Vahid Bagheri & Ahmad Keshavarz

  2. Computer Engineering Department, School of Engineering, Persian Gulf University, Bushehr, 75168, Iran

    Habib Rostami

Authors
  1. Mehdi Shadlou Jahromi
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  2. Vahid Bagheri
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  3. Habib Rostami
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  4. Ahmad Keshavarz
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Correspondence to Habib Rostami.

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Shadlou Jahromi, M., Bagheri, V., Rostami, H. et al. Feature Extraction in Fractional Fourier Domain for Classification of Passive Sonar Signals. J Sign Process Syst 91, 511–520 (2019). https://doi.org/10.1007/s11265-018-1347-x

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  • DOI: https://doi.org/10.1007/s11265-018-1347-x

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