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Underwater Sound Source Localization by EMD-Based Maximum Likelihood Method

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Abstract

The underwater object localization is important in defense, underwater biological and environmental applications. Localization using a passive sonar system is a challenging task. It is more challenging when the source and receivers are in the reverberant environment. Time delay estimation (TDE)-based localization is a well-known technique to localize source for last few decades. In this work, empirical mode decomposition maximum likelihood (EMD ML TDE) method is used to estimate the time delay in a reverberant environment. The sound source location is estimated by intersecting spherical surfaces from the time delay. The experimental results prove that EMD ML time delay estimation method is effective to localize a sound source in a reverberant environment.

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References

  1. Zhang, C., et al.: Maximum likelihood sound source localization and beamforming for directional microphone arrays in distributed meetings. IEEE Trans. Multimed. 10, 538–548 (2008)

    Article  Google Scholar 

  2. Valin, J.M., Michaud, F., Hadjou, B., Rouat, J.: Localization of simultaneous moving sound sources for mobile robot using a frequency-domain steered beamformer approach. IEEE Int. Conf. Robot. Autom. Proc. 1, 1033–1038 (2004)

    Google Scholar 

  3. DiBiase, J.H., Silverman, H.F., Brandstein, M.S.: Robust localization in reverberant rooms. Microphone Arrays, pp. 157–180. Springer, Berlin (2001)

    Book  Google Scholar 

  4. Argentieri, S., Danes, P.: Broadband variations of the MUSIC high-resolution method for sound source localization in robotics. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. (2007)

  5. Asano, F., Goto, M., Itou, K., Asoh, H.: Real-time sound source localization and separation system and its application to automatic speech recognition. Seventh European Conference on Speech Communication and Technology. (2001)

  6. Duan, R., Yang, K., Ma, Y.: Narrowband source localisation in the deep Ocean using a near-surface array. Acoustics Australia 42(1), 36–42 (2014)

    Google Scholar 

  7. Yang, K., Lu, Y., Lei, Z., Xia, H.: Passive localization based on multipath time-delay difference with two hydrophones in deep Ocean. Acoustics Australia 45(1), 51–60 (2017)

    Article  Google Scholar 

  8. Schau, H.C., Robinson, A.Z.: Passive source localization employing intersecting spherical surfaces from time-of-arrival differences. IEEE Trans. Acoust. Speech Signal Process. 35(8), 1223–1225 (1987)

    Article  Google Scholar 

  9. Wang, H., Chu, P.: Voice source localization for automatic camera pointing system in videoconferencing. IEEE Int. Conf. Acoustics Speech Signal Process. 1, 187–190 (1997)

    Google Scholar 

  10. Omologo, M., Svaizer, P.: Acoustic event localization using a crosspower-spectrum phase based technique. IEEE Int. Conf. Acoustics Speech Signal Process. 2, 273–276 (1994)

    Google Scholar 

  11. Chen, J., Huang, Y., Benesty, J.: Time delay estimation, In: Audio signal processing for next-generation multimedia communication systems. Springer, 197–227 (2004)

  12. Gedalyahu, K., Eldar, Y.C.: Time-delay estimation from low-rate samples: a union of subspaces approach. IEEE Trans. Signal Process. 58(6), 3017–3031 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Qasaymeh, M.M., Gami, H., Tayem, N., Sawan, M.E., Pendse, R.: Joint time delay and frequency estimation without eigen-decomposition. IEEE Signal Process. Lett. 16(5), 422–425 (2009)

    Article  Google Scholar 

  14. Liang, Y.C., Leyman, A.R.: Time delay estimation using higher order statistics. Electron. Lett. 33(9), 751–753 (1997)

    Article  Google Scholar 

  15. Hinich, M.J., Wilson, G.R.: Time delay estimation using the cross bispectrum. IEEE Trans. Signal Process. 40(1), 106–113 (1992)

    Article  Google Scholar 

  16. Sharma, K.K., Joshi, S.D.: Time delay estimation using fractional fourier transform. Signal Process. 87(5), 853–865 (2007)

    Article  MATH  Google Scholar 

  17. Zhou, T., Li, H., Zhu, J., Xu, C.: Subsample time delay estimation of chirp signals using frft. Signal Process. 96, 110–117 (2014)

    Article  Google Scholar 

  18. Lui, K.W., Chan, F.K., So, H.C.: Accurate time delay estimation based passive localization. Signal Process. 89(9), 1835–1838 (2009)

    Article  MATH  Google Scholar 

  19. Knapp, C.H., Carter, G.C.: The generalized correlation method for estimation of time delay. IEEE Trans. Acoust. Speech Signal Process. 24(4), 320–327 (1976)

    Article  Google Scholar 

  20. Dhull, S., Arya, S., Sahu, O.: Comparison of time-delay estimation techniques in acoustic environment. Int. J. Comput. Appl. 8(9), 29–31 (2010)

    Google Scholar 

  21. Stephenne, A., Champagne, B.: Cepstral prefiltering for time delay estimation in reverberant environments. Acoustics, Speech, and Signal Processing, ICASSP, 3055–3058 (1995)

  22. Lyon, D.: The discrete fourier transform, part 6: Cross-correlation. J. Object Technol. 9(2), 17–22 (2010)

    Article  Google Scholar 

  23. Carter, G.C., Nuttall, A.H., Cable, P.G.: The smoothed coherence transform. Proc. IEEE 61(10), 1497–1498 (1973)

    Article  Google Scholar 

  24. Roth, P.R.: Effective measurements using digital signal analysis. IEEE Spectr. 4(8), 62–70 (1971)

    Article  Google Scholar 

  25. Hannan, E., Thomson, P.: Estimating group delay. Biometrika 60(2), 241–253 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  26. Bedard, S., Champagne, B., Stephenne, A.: Effects of room reverberation on time-delay estimation performance. IEEE Int. Conf. Acoustics Speech Signal Process. 2, 261 (1994)

    Google Scholar 

  27. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.C., Tung, C. C., Liu, H. H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 903–995 (1998)

  28. Lei, Y., Lin, J., He, Z., Zuo, M.J.: A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 35(1), 108–126 (2013)

    Article  Google Scholar 

  29. Kopsinis, Y., McLaughlin, S.: Development of emd-based denoising methods inspired by wavelet thresholding. IEEE Trans. Signal Process. 57(4), 1351–1362 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  30. Boudraa, A.O., Cexus, J.C.: Denoising via empirical mode decomposition. Proc. IEEE ISCCSP. 4, (2006)

  31. Weng, B., Blanco-Velasco, M., Barner, K.E.: Ecg denoising based on the empirical mode decomposition. Engineering in Medicine and Biology Society. (EMBS06), 1–4 (2006)

  32. Rai, V.K., Mohanty, A.R.: Bearing fault diagnosis using fft of intrinsic mode functions in hilberthuang transform. Mech. Syst. Signal Process. 21(6), 2607–2615 (2007)

    Article  Google Scholar 

  33. Tsypkin, M.: Induction motor condition monitoring: Vibration analysis technique a twice line frequency component as a diagnostic tool. In: IEEE-International Electric Machines Drives Conference (IEMDC), 117–124 (2013)

  34. Mohanty, A.R., Kar, C.: Fault detection in a multistage gearbox by demodulation of motor current waveform. IEEE Trans. Industr. Electron. 53(4), 1285–1297 (2006)

    Article  Google Scholar 

  35. Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Mechanical Engineering Department, Indian Institute of Technology, Kharagpur, India

    B Marxim Rahula Bharathi & A R Mohanty

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  1. B Marxim Rahula Bharathi
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  2. A R Mohanty
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Correspondence to B Marxim Rahula Bharathi.

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Marxim Rahula Bharathi, B., Mohanty, A.R. Underwater Sound Source Localization by EMD-Based Maximum Likelihood Method. Acoust Aust 46, 193–203 (2018). https://doi.org/10.1007/s40857-018-0129-8

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