Using broadband acoustic signals and modal behavior to assess scales of ocean variability in shallow water waveguides

2015 ◽  
Vol 138 (3) ◽  
pp. 1934-1934
Author(s):  
Mohsen Badiey ◽  
Marshall H. Orr
Keyword(s):  
Shallow Water ◽  
Acoustic Signals ◽  
Ocean Variability ◽  
Modal Behavior

Time variability of acoustic signals in a benign shallow‐water area

2000 ◽  
Vol 108 (5) ◽  
pp. 2578-2578
Author(s):  
Peter L. Nielsen ◽  
Martin Siderius ◽  
Finn B. Jensen
Keyword(s):  
Shallow Water ◽  
Acoustic Signals ◽  
Water Area ◽  
Time Variability ◽  
Shallow Water Area

Classification of shallow-water acoustic signals via alpha-Stable modeling of the one-dimensional wavelet coefficients

2006 ◽  
Vol 119 (3) ◽  
pp. 1396-1405 ◽  
Author(s):  
Michael I. Taroudakis ◽  
George Tzagkarakis ◽  
Panagiotis Tsakalides
Keyword(s):  
Shallow Water ◽  
Acoustic Signals ◽  
Wavelet Coefficients ◽  
One Dimensional ◽  
The One

Hardware Implementation of Kalman Least Mean Square-Based Adaptive Algorithm for Denoising Ambient Noises in Shallow Water Region

2014 ◽  
Vol 13 (03) ◽  
pp. 1450018
Author(s):  
S. Sakthivel Murugan ◽  
V. Natarajan ◽  
S. Prethivika
Keyword(s):  
Shallow Water ◽  
Ambient Noise ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Useful Signal ◽  
Custom Made ◽  
Source Signal

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noise ratio (SNR) and mean square error (MSE) for various wind speeds. An exhaustive set of data, collected using a custom made fixture containing two hydrophones, from shallow water regions in Bay of Bengal, have been used to verify the efficacy of this method. Based on the results obtained by simulation and Lab window simulator, hardware has been designed to denoise the useful signal. The defective source signal is passed through a Kalman filter based denoising hardware system. This system performs necessary operations to denoise the defective source signal and the final turnout is made free from ambient noise. The denoised signal is then stored in an external device for future use.

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