Estimation of Noise Model and Denoising of Wind Driven Ambient Noise in Shallow Water Using the Lms Algorithm

INTRODUCTION Signal transmission underwater is a challenging task. Generally, low frequency acoustic signals are used for transmission underwater as electromagnetic signals are highly attenuated. Any modulated signal transmitted in water, undergoes various losses due to attenuation, reverberation, spreading and internal waves etc apart from ambient noise due to natural and manmade sources. The residual noise background in the absence of individual identifi able sources may be considered as the natural noise environment for hydrophone sensors. It comprises a number of components that contribute to the Noise Level (NL) in varying degrees depending on the location of measurements [1]. The sources contributing noise include geological disturbances, non-linear wave interaction, turbulent wind stress on the sea surface, shipping, distant storms, seismic prospecting, marine animals, breaking waves, spray, rain, hail impacts and turbulence [2]. The ambient noise level spectrum is summarized in [3]. Furthermore Knudsen spectra [4] show the strong dependence of spectral power level with wind speed and sea states. Noise measurements made in the Northern Hemisphere show self-similar wind dependent noise spectra between 100 Hz and 10 kHz [3,4], but no dependency on wind speed below 100 Hz, with noise at these lower frequencies being attributable to distant shipping. Measurements made at 40 different locations in the Southern Hemisphere showed that in regions of low shipping density the effect of wind speed is dominant in the frequency band of 22 Hz to 5 kHz [5]. The ambient noise masks the signals from underwater acoustic instruments, so the detection and cancellation of background noise is essential to enhance the SNR of acoustic based underwater instruments. This can be done by a proper adaptive fi lter implementation [6,7]. In this paper, an LMS based adaptive algorithm to denoise the received signal is implemented.