Abstract
Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition (EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions (IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper a new EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region. Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithm identifies and extracts the reference signals against various ambient noises. Significant SNR improvement is also achieved for underwater acoustic signals.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Blakely, C. D., 2005. A Fast Empirical Mode Decomposition Technique for Nonstationary Nonlinear Time Series, Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park MD 20740 USA, Elsevier Science 3rd Ed., 1–14.
Boudraa, A. O., 2007. EMD based signal filtering, IEEE Transactions on Instrumentations and Measurements, 56(6): 2196–2202.
Delechelle, E., Lemonie, J. and Niang, O., 2005. Empirical mode decomposition: An analytical approach for sifting process, IEEE Signal Processing Letters, 12(11): 764–767.
Karagiannis, A. and Constantinou, P., 2011. Noise-assisted data processing with empirical mode decomposition in biomedical signals, IEEE Transactions on Information Technology in Biomedicine, 15(1): 11–18.
Kasolvsky, D. N. and Meyer, F. G., 2010. Noise corruption of empirical mode decomposition and its effect on instantaneous frequency, Advances in Adaptive Data Analysis, 2(3): 376–393.
Kim, D. and Oh, H. S., 2009. EMD: A package for empirical mode decomposition and Hilbert spectrum, The R Journal, 1(1): 40–46.
Rilling, G. and Flandrin, P., 2008. One or two frequencies the empirical mode decomposition answers, IEEE Transactions on Signal Processing, 56(1): 85–95.
Wang, X. J., Feng, G. L. and Feng, A. X., 2011. On performance difference of EMD and WD in the nonlinear time series analysis, Proceedings of the Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 1, 207–211.
Zhang, Y. K., Ma, X. C., Hua, D. X., Cui, Y. A. and Sui, L. S., 2010. An EMD-based denoising method for Lidar Signal, Proceedings of the 3rd International Congress on Image and Signal Processing, 8, 4016–4019.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Murugan, S.S., Natarajan, V. & Maheswaran, K. Analysis of EMD algorithm for identification and extraction of an acoustic signal in underwater channel against wind driven ambient noise. China Ocean Eng 28, 645–657 (2014). https://doi.org/10.1007/s13344-014-0051-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13344-014-0051-2