scholarly journals Passive Source Localization Using Acoustic Intensity in Multipath-Dominant Shallow-Water Waveguide

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2198
Author(s):  
Sunhyo Kim ◽  
Sungho Cho ◽  
Seom-kyu Jung ◽  
Jee Woong Choi
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Large Scale ◽  
Finite Difference Approximation ◽  
Difference Approximation ◽  
Water Medium ◽  
Acoustic Intensity ◽  
Invariant Method ◽  
Vector Quantity ◽  
Passive Source Localization

The array invariant technique has been recently proposed for passive source localization in the ocean. It has successfully estimated the source–receiver horizontal range in multipath-dominant shallow-water waveguides. However, it requires a relatively large-scale hydrophone array. This study proposes an array invariant method that uses acoustic intensity, which is a vector quantity that has the same direction as the sound wave propagating through a water medium. This method can be used to estimate not only the source–receiver horizontal range, but also the azimuth to an acoustic source. The feasibility of using a vector quantity for the array invariant method is examined through a simulation and an acoustic experiment in which particle velocity signals are obtained using a finite difference approximation of the pressure signals at two adjacent points. The source localization results estimated using acoustic intensity are compared with those obtained from beamforming of the acoustic signals acquired by the vertical line array.

RECENT DEVELOPMENTS IN THE NUMERICAL SIMULATION OF SHALLOW WATER EQUATIONS II: TEMPORAL DISCRETIZATION

1994 ◽  
Vol 04 (04) ◽  
pp. 533-556 ◽  
Author(s):  
V. AGOSHKOV ◽  
E. OVCHINNIKOV ◽  
A. QUARTERONI ◽  
F. SALERI
Keyword(s):  
Numerical Simulation ◽  
Finite Element ◽  
Finite Difference ◽  
Shallow Water ◽  
Shallow Water Equations ◽  
Numerical Results ◽  
Finite Difference Approximation ◽  
Difference Approximation ◽  
Recent Developments ◽  
Numerical Approaches

This paper deals with time-advancing schemes for shallow water equations. We review some of the existing numerical approaches, propose new schemes and investigate their stability. We present numerical results obtained using the time-advancing schemes proposed, with finite element and finite difference approximation in space variables.

Peak Extraction Passive Source Localization Using a Single Hydrophone in Shallow Water

2020 ◽  
Vol 69 (3) ◽  
pp. 3412-3423
Author(s):  
Tongwei Zhang ◽  
Guangjie Han ◽  
Mohsen Guizani ◽  
Lei Yan ◽  
Lei Shu
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Passive Source Localization

scholarly journals Numerical Methods for Information Tracking of Noisy and Non-smooth Data in Large-scale Statistics

2019 ◽  
pp. 1-16
Author(s):  
B. S. Avinash ◽  
T. Srisupattarawanit ◽  
H. Ostermeyer
Keyword(s):  
Adverse Effect ◽  
Data Analysis ◽  
Finite Difference ◽  
Large Scale ◽  
Noisy Data ◽  
Vital Role ◽  
Finite Difference Approximation ◽  
Difference Approximation ◽  
Regularization Methods ◽  
Analysis Process

In our universe, there is a presence of random bit of disorder in every field that has to be contemplated and understood clearly. This random bit of disorder in a physical system is known as noise. Noise in the field of statistics can be defined as an additional meaningless information that cannot be clearly interpreted which is present in the entire dataset. In large-scale statistics, noisy data has an adverse effect on the results and it can lead to skewness in any data analysis process, if not properly understood or handled. The adverse effect on the results is mainly due to uncorrelated (zero autocorrelation) property of noise. This makes it completely unpredictable at any given point in time, hence thorough investigation and removal of noise plays a vital role in data analysis process. In the field of engineering, measurement of experimental data obtained by using scientific instruments consists of some values that are independent of the experimental setup. One of most widely technique is the optimization methods viz, gradient descent, conjugate gradient, Newton’s method etc. Most of these methods require the determination of derivative of a function specified by the dataset (using finite-difference approximation). If the noisy data is approximated using a specific finite difference method this results in the amplification of noise present in the data. In order to overcome the aforementioned problem of amplification of noise in the derivative of a function, various regularization methods are employed. The parameter that plays a vital role in these methods are termed as regularization parameter. One of the most important technique used in the field of regularization is known as total variation regularization. This review aimed at gathering the disperse literature on the current state of various noises and their regularization methods.

scholarly journals A large-scale numerical model for computing isochrone geometry

2009 ◽  
Vol 50 (51) ◽  
pp. 130-140 ◽  
Author(s):  
Richard C.A. Hindmarsh ◽  
Gwendolyn J.-M.C. Leysinger Vieli ◽  
Frédéric Parrenin
Keyword(s):  
Finite Difference ◽  
Heat Equation ◽  
Large Scale ◽  
Linear Equations ◽  
Finite Difference Approximation ◽  
Difference Approximation ◽  
Preconditioned Conjugate Gradient ◽  
Large Set ◽  
Vertical Coordinate ◽  
Vertical Distributions

AbstractA finite-difference model for the calculation of radar layer geometries in large ice masses is presented. Balance velocities are used as coefficients in the age equation and in the heat equation. Solution of the heat equation allows prediction of sliding areas and computation of basal melt rates. Vertical distributions of velocity are parameterized using shape functions. These can be set uniformly, or allowed to vary in space according to the distribution of sliding. The vertical coordinate can either be uniformly distributed within the thickness of the ice, or be uniformly distributed within the flux. The finite-difference scheme results in a large set of linear equations. These are solved using a nested factorization preconditioned conjugate gradient scheme. The convergence properties of some other iteration solution schemes are studied. The output is computations of age and temperature assuming steady state, in large ice masses at high resolution. Age calculations are used to generate isochrones which show the best fit to observed layers. Comparisons with analytical solutions are made, and the influence of the order of the finite-difference approximation and the choice of vertical coordinate on solution accuracy is considered.

scholarly journals Matched-field Source Localization with a Mobile Short Horizontal Linear Array in Offshore Shallow Water

2013 ◽  
Vol 38 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Dexin Zhao ◽  
Zhiping Huang ◽  
Shaojing Su ◽  
Ting Li
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Linear Array ◽  
Autonomous Underwater Vehicles ◽  
Field Source ◽  
Position Errors ◽  
Straight Line ◽  
Synthetic Test ◽  
Passive Source Localization ◽  
Localization Performance

Abstract Passive source localization in shallow water has always been an important and challenging problem. Implementing scientific research, surveying, and monitoring using a short, less than ten meter long, horizontal linear array has received considerable attention in the recent years. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, it is usually difficult to obtain a sufficient spatial gain for localizing long-range sources due to its limited physical size. To address this problem, a localization approach is proposed which is based on matched-field processing of the likelihood of the passive source localization in shallow water, as well as inter-position processing for the improved localization performance and the enhanced stability of the estimation process. The ability of the proposed approach is examined through the two-dimensional synthetic test cases which involves ocean environmental mismatch and position errors of the short array. The presented results illustrate the localization performance for various source locations at different signal- to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the short array moves at a low speed along a straight line at a certain depth.

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