An underwater acoustic sound velocity data model

LeBlanc, Lester R.; Middleton, F. H.

doi:10.1121/1.384448

Cited by 116 publications

(53 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the equivalent time delay error dq ti is corrected but the sound ray tracing is not implemented in the calculation of measurement distances, based on the impact of sound velocity on the measurement distances, the equivalent SVP error can be considered as a constant and estimated when incident angles of these measurement distances are changeless [9]. The coefficient matrix changes from B to F as Eq.…”

Section: The Improved Intersection Positioning Modelmentioning

confidence: 99%

A new method for absolute datum transfer in seafloor control network measurement

et al. 2015

View full text Add to dashboard Cite

To decrease the time consumption and the labor intensity in the absolute datum transfer of traditional seafloor control network measurement, a new method, namely sailing-circle positioning method, is put forward in this paper. First, the traditional intersection positioning model is improved by considering the equivalent sound velocity profile error as an unknown parameter in the adjustment model. Second, the effect of geometric dilution of precision (GDOP) on positioning accuracy is analyzed. By seeking for the minimum of GDOP, it is concluded that the absolute datum transfer can achieve the highest accuracy in the condition of sailing along a circle relative to other sailing paths. Moreover, the optimal radius of the circle for the accurate datum transfer is also given out. Besides, the correlation between the accuracy of datum transfer and the sound velocity error in this method is analyzed. Finally, the new method was tested and verified by the experiments in Songhua lake with the water depth of 60 m and in South China sea with the water depth of 2000 m, respectively. These experiment results show that the new method can improve the accuracy and efficiency of traditional datum transfer method significantly.

show abstract

Section: The Improved Intersection Positioning Modelmentioning

confidence: 99%

A new method for absolute datum transfer in seafloor control network measurement

et al. 2015

View full text Add to dashboard Cite

show abstract

“…With this depth notation, we can partition the covariance matrix of secondary data into different depth ranges, i.e., (2) where the element or is the autocovariance or cross covariance between the secondary data in the depth ranges and , and . Simple matrix manipulation also shows that (3) where the subscribed indicates the portion of the EOFs in the corresponding depth range ( or ).…”

Section: A Descriptionmentioning

confidence: 99%

“…For example, LeBlanc and Middleton [3] first applied EOF analysis to sound-speed data in the Atlantic Ocean. Also, Newhall et al [4] used a spatial EOF interpolation to construct a 3-D sound-speed field for acoustic ray tracing modeling.…”

mentioning

confidence: 99%

Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

Lin

Newhall

Duda

et al. 2010

IEEE J. Oceanic Eng.

View full text Add to dashboard Cite

Abstract-In this paper, a method for merging partial overlapping time series of ocean profiles into a single time series of profiles using empirical orthogonal function (EOF) decomposition with the objective analysis is presented. The method is used to handle internal waves passing two or more mooring locations from multiple directions, a situation where patterns of variability cannot be accounted for with a simple time lag. Data from one mooring are decomposed into linear combination of EOFs. Objective analysis using data from another mooring and these patterns is then used to build the necessary profile for merging the data, which is a linear combination of the EOFs. This method is applied to temperature data collected at a two vertical moorings in the 2006 New Jersey Shelf Shallow Water Experiment (SW06). Resulting profiles specify conditions for 35 days from sea surface to seafloor at a primary site and allow for reliable acoustic propagation modeling, mode decomposition, and beamforming.

show abstract

“…EOFs are eigenvectors of the SSP covariance matrix which is usually estimated from onsite and/or historical SSP measurements. 32 Each eigenvector represents one mode of the SSP variation with depth, while the corresponding eigenvalue indicates the amount of energy in that mode. A range-averaged SSP can be described by a mean profile, cðzÞ (z is depth), plus some zero-mean random perturbations, which are often expressed in terms of a set of EOFs, so that…”

Section: A Empirical Orthogonal Function Representation Of Sspmentioning

confidence: 99%

“…In the water column, EOFs are derived from direct measurements of the SSP and they are orthogonal in regard to the statistics of the SSP variations. 32 Tracking capabilities of the PF, the EnKF, and the EnKPF under slowly and quickly changing SSPs are compared in terms of divergence statistics with synthetic acoustic pressure data and experimental SSP data collected during the PRIMER experiment and the ASIAEX (Asian Seas International Acoustics Experiment) ECS (East China Sea) 2001 experiment, respectively. Because the running time is an important fact in the tracking problem, the complexity analysis of the algorithms is also performed.…”

Section: Introductionmentioning

confidence: 99%

Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter

Zhou

2013

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

This paper presents a tracking technique for performing sequential geoacoustic inversion monitoring range-independent environmental parameters in shallow water. The inverse problem is formulated in a state-space model with a state equation for the time-evolving sound speed profile (SSP) and a measurement equation that incorporates acoustic measurements via a hydrophone array. The particle filter (PF) is an ideal algorithm to perform tracking of environmental parameters for nonlinear systems with non-Gaussian probability densities. However, it has the problem of the mismatch between the proposal distribution and the a posterior probability distribution (PPD). The ensemble Kalman filter (EnKF) can obtain the PPD based on the Bayes theorem. A tracking algorithm improves the performance of the PF by employing the PPD of the EnKF as the proposal distribution of the PF. Tracking capabilities of this filter, the EnKF and the PF are compared with synthetic acoustic pressure data and experimental SSP data. Simulation results show the proposed method enables the continuous tracking of the range-independent SSP and outperforms the PF and the EnKF. Moreover, the complexity analysis is performed, and the computational complexity of the proposed method is greatly increased because of the combination of the PF and the EnKF.

show abstract

An underwater acoustic sound velocity data model

Cited by 116 publications

References 0 publications

A new method for absolute datum transfer in seafloor control network measurement

A new method for absolute datum transfer in seafloor control network measurement

Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter

Contact Info

Product

Resources

About