Publication Type:

Conference Paper


2011 International Symposium on Ocean Electronics (2011)


acoustic signal, adaptive algorithm analysis, Adaptive algorithms, Adaptive filters, Algorithm design and analysis, ambient noise, Bartlett estimation method, Bay of Bengal, Blackman estimation method, Chennai, electromagnetic wave transmission, Estimation, high attenuation nature, Indian seas, Least squares approximation, low frequency acoustic signal, noise, oceanographic techniques, real time data, recursive mean square, RLS, shallow water, Signal to noise ratio, signal-to-noise ratio, SNR, SNR values, Spectral Estimation, underwater acoustic propagation, underwater signal transmission, Welch estimation method, wind effect, wind speeds


Underwater signal transmission is a challenging task since the usable frequency range is limited to low frequency and the transmission of electromagnetic waves is impossible due to its high attenuation nature. Hence low frequency acoustic signal is more suited for transmission in underwater. Underwater transmission is highly affected by wind noise which is predominant at low frequency. The real time data collected from Indian Seas at Chennai (Bay of Bengal) are studied in detail using Welch, Bartlett and Blackman estimation methods and the results shows the effect of wind over 0-8 kHz range. Various adaptive algorithms are analyzed in detail and the Signal to Noise Ratio (SNR) values are tabulated for different wind speeds. The results shows that Recursive Mean Square (RLS) works better when compared to others. The maximum Signal to Noise Ratio (SNR) of about 42-51 dB is achieved.

Cite this Research Publication

S. S. Murugan, Natarajan, V., S. Veni, and Balagayathri, K., “Analysis of adaptive algorithms to improve the SNR of the acoustic signal affected due to wind driven ambient noise in shallow water”, in 2011 International Symposium on Ocean Electronics, 2011.