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Feature Article

Algorithms for Underwater Acoustic Communications

By S. Sakthivel Murugan Assistant Professor S. Radha Professor and Head Electronics and Communication Engineering Department SSN College of Engineering Chennai, India and V. Natarajan Associate Professor Instrumentation Department Madras Institute of Technology, Anna University Chennai, India

Underwater acoustic communications research is rapidly growing as commercial and military applications increase. Adaptive filters can alleviate the degradation caused by wind-driven ambient noise in shallow water. Because underwater acoustic signals are greatly affected by ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signals.

When an acoustic signal is transmitted through water, apart from various types of losses incurred, like reverberation, attenuation, absorption and scattering, the acoustic signal also undergoes distortion. The sources for ambient noises that cause this can be natural (rain, wind, seismic, mammals, etc.) or man-made (ships, boats, harbor activities, aircraft, etc.).

The effect of ambient noises has been found to be due to various sources occupying different frequency ranges. Wind noise has been proven by the researchers Cato Knudsen and R.J. Urick in a study of ambient noise to exist at low frequencies, ranging from 100 hertz to 6 kilohertz, and is predominant only at very low frequencies. Its effect is found to be almost insignificant above 6 kilohertz. Hence, any acoustic signal transmitted in the low-frequency range is affected by wind noise.

The following are several possible adaptive filtering approaches for denoising the signal affected by wind-driven ambient noise, which results in significant SNR improvement. Least mean square (LMS), normalized LMS (NLMS), modified new LMS (MNLMS) and Kalman LMS (KLMS) algorithms are included.

Adaptive Filtering Structure
Adaptive filters are digital filters that can alter their coefficients based on adaptive algorithms and are used in situations where knowledge for time-varying signals is unknown. The adaptive filter has a feedback in the form of an error signal used to adjust its transfer function to match the changing parameters.

Many computationally efficient algorithms for adaptive filtering have been developed. Adaptive filtering algorithms generally consist of two basic processes: a filtering process and an adaptive one. A filtering process involves computation of output of a linear filter in response to an input signal and generation of an estimation error by comparing the output with a desired response. An adaptive process involves automatic adjustment of the filter's parameters in accordance with the estimation error. These two processes working together constitute a feedback loop. The order of the filter determines the number of samples processed in each iteration.

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S. Sakthivel Murugan is an assistant professor of electronics and communication engineering at SSN College of Engineering. His research includes work on underwater acoustic signal processing and underwater noise modeling.

S. Radha, professor and head of the SSN College of Engineering's Department of Electronics and Communication Engineering, has 22 years of teaching and 11 years of research experience in mobile ad-hoc networks.

V. Natarajan is an associate professor at the instrumentation department of Madras Institute of Technology, Anna University. He has 25 years of teaching experience and 10 years in research.

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