Deep-Towed Array Geophysical System

1984 ◽  
Author(s):  
S.K. Jones ◽  
S.E. Spychalski ◽  
M.G. Fagot
Keyword(s):  
Towed Array ◽  
Geophysical System

Long-range acoustic communication in deep water using a towed array

2011 ◽  
Vol 129 (3) ◽  
pp. EL71-EL75 ◽  
Author(s):  
H. C. Song ◽  
S. Cho ◽  
T. Kang ◽  
W. S. Hodgkiss ◽  
J. R. Preston
Keyword(s):  
Long Range ◽  
Deep Water ◽  
Acoustic Communication ◽  
Towed Array

Wavelet Entropy Energy Measure (WEEM): A multiscale measure to grade a geophysical system's predictability

2021 ◽  
Author(s):  
Ravi Kumar Guntu ◽  
Ankit Agarwal
Keyword(s):  
Time Series ◽  
Energy Distribution ◽  
Energy Concentration ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Wavelet Entropy ◽  
Wavelet Energy ◽  
Energy Measure ◽  
Geophysical Time Series ◽  
Geophysical System

<p>Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution. The proposed metric is normalized, handles non-stationarity, independent of the length of the data. Therefore, it can explain the evolution of predictability for any geophysical time series (ex: precipitation, streamflow, paleoclimate series) from past to the present. WEEM metric's performance can guide the forecasting models in getting the best possible prediction of a geophysical system by comparing different methods. </p>

scholarly journals Identification and Removal of Non-acoustic Noise in Towed Array Sonar Using F-Κ Transform for Enhanced Torpedo Detection

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Vibration Isolation ◽  
Acoustic Noise ◽  
Detection System ◽  
Mechanical Vibration ◽  
Low Frequency ◽  
Acoustic Energy ◽  
Noise Sources ◽  
Towed Array ◽  
Spatio Temporal ◽  
Towed Arrays

Low frequency passive towed array sonar is an essential component in a torpedo detection system for surface ships. Compact towed arrays are used for torpedo detection and they will be towed at higher towing speeds compared to conventional towed array sonars used for surveillance. Presence of non-acoustic noise in towed array sensors at higher towing speeds degrades torpedo detection capability at lower frequencies. High wavenumber mechanical vibrations are induced in the array by vortex shedding associated with hydrodynamic flow over the array body and cable scope. These vibrations are known to couple into the hydrophone array as nonacoustic noise sources and can impair acoustic detection performance, particularly in the forward end fire direction. Lengthy mechanical vibration isolation modules can isolate vibration induced noise in towed arrays, but this is not recommended in a towed array which is towed at high speeds as it will increase the drag and system complexity. An algorithm for decomposing acoustic and non-acoustic components of signals received at sensor level using well known frequency-wavenumber transform (F-K transform) is presented here. Frequency-wavenumber diagrams can be used for differentiating between acoustic and non-acoustic signals. An area of V shape is identified within the F-K spectrum where acoustic energy is confined. Energy outside this V will highlight non-acoustic energy. Enhanced simultaneous spatio-temporal and spatio-amplitude detection is possible with this algorithm. Performance of this algorithm is validated through simulation and experimental data.

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