Acoustic Propagation Uncertainty and Probabilistic Prediction of Sonar System Performance in the Southern East China Sea Continental Shelf and Shelfbreak Environments

Emerson, Chris; Lynch, James F.; Abbot, Philip; Lin, Ying‐Tsong; Duda, Timothy F.; Gawarkiewicz, Glen; Chen, Chi‐Fang

doi:10.1109/joe.2014.2362820

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…This follows up an earlier experimentally-based QPE paper on acoustic TL variability in a different canyon (Chiu et al, 2011). The second article (Emerson et al, 2013), shows that 800-1000 Hz acoustic signals traveling up to 10 km were measured to have short-term TL variability of 1.5 to 2 dB, which is perhaps a bit less than measured in SW06 (Lynch et al, 2012). Combined with noise variability in the area of up to 6 dB rms, the signal excess for underwater sound in the area is expected to have a greater than 6 dB rms variability.…”

Section: Shallow-water Effort Qpementioning

confidence: 76%

“…The paper further notes that a rapid drop-off of received sound level from the moving source for a path length of a few kilometers coincides with arrival of nonlinear internal waves, but is not consistent with expectations for the OMAS moving through the predicted 3D spatial beam/shadowing patterns. The other paper covered mean transmission loss (TL) versus range curves and TL fluctuation probability distributions for a set of OMAS transmission experiments on the shelf (Emerson et al, 2013). Ambient noise and signal excess are also examined.…”

Section: Shallow Water Effortmentioning

confidence: 99%

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Experimentally-Based Ocean Acoustic Propagation and Coherence Studies

Duda¹

2013

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The goal is to better understand fluctuating sound propagation in two distinct ocean acoustic regimes: Stratified shallow water, where sound is highly bottom interacting, and the temperate deep-ocean sound channel. Acoustic field fluctuations have time scales varying from less than a minute to hours, and horizontal spatial scales from tens of meters to kilometers, comparable to processing time scales and system spatial scales, and thus impact exploitation of underwater sound. With proper understanding, reliable predictions of temporal and spatial variability of received underwater sound may be possible, thus improving processing and handling of signals of interest. Processing could include remediation of signal degradation and/or exploitation of available sonic information. OBJECTIVES An objective is to quantify and explain underwater sound fluctuation behavior that has been observed in both shallow and deep regimes, for the purpose of meeting the stated goals. For shallow water, transmission loss and phase data are available at frequencies from 50 to 3000 Hz. For the deep-ocean sound channel, data are in hand for propagation at 50 to 100 Hz over 100's to 1000's of kilometers. Propagation and forward scattering models, both theoretical and computational, will be used to test hypotheses regarding the origin of signal variations. Therefore, making better models is a second (enabling) objective. APPROACH The approach toward understanding the fundamentals of ocean sound propagation variability in numerous environmental regimes is to study the propagation together with the local environmental conditions. First, acoustically important features, such as nonlinear internal waves in the shallow seas and quasi-homogeneous internal waves in the deep seas, must be identified in the coupled studies. Next, the acoustic phenomena caused by those features are studied in detail, to identify the feature properties that are relevant to acoustics. Next, those properties are investigated, with a purpose of extrapolating (predicting) acoustic behavior in parameter regimes that may not have been encountered. The investigations of physical ocean phenomena that are critical to this approach are likely to differ from studies of the features motivated by other branches of ocean science, such as studies of biogeochemical/physical cycling, fisheries, or climate.

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Section: Shallow-water Effort Qpementioning

confidence: 76%

Section: Shallow Water Effortmentioning

confidence: 99%

Experimentally-Based Ocean Acoustic Propagation and Coherence Studies

Duda¹

2013

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“…A. PROOF OF (6), (7) AND (8) The 4-DOF measurement and control platform coordinate system oxyz and the angle quantities, such as θ x , θ y , θ h , θ z , ϕ z , and related length quantities are all shown in Fig.15. From the geometric relationship in the upper graph, the following relations can be obtained:…”

Section: Appendixmentioning

confidence: 99%

“…The same to other underwater acoustic equipment, the final criterion of underwater acoustic positioning system is also the effectiveness under operational conditions. However, operational tests of the underwater acoustic positioning system are not only expensive and time-consuming, but also difficult to control the measurement conditions in the field conditions [5], such as uncertainty caused by climate impact factors, water medium inhomogeneity, measurement platform sloshing, multi-factor coupling and so on [6]- [8]. At the same time, maintenance of the outdoor calibration and measuring system is also one of the problems [9].…”

Section: Introduction a Background And Related Workmentioning

confidence: 99%

Metering Method and Measurement Uncertainty Evaluation of Underwater Positioning System in Six Degrees of Freedom Space

et al. 2020

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Meterage is an important action to ensure the performance of underwater acoustic positioning system, but the commonly used measurement methods are mostly based on the low degrees of freedom (DOF) measurement and control platforms which could not realize the all-round and high efficiency evaluation of this equipment. In this paper, we develop a new metering method base on the conventional standard 4-DOF measurement and control platform to realize the whole exploration space high efficiency and high accuracy measurement of underwater acoustic positioning systems in 6-DOF space. First, a 4-DOF space to 6-DOF space quantity value propagation model is proposed, and a rigorous mathematical expression is established. Next, a measurement procedure is formed based on the building of a standard base-line field, so the channel of transforming measurable position coordinate of the measured field point in 4-DOF space to the 6-DOF space is established. Finally, the evaluation of the measurement uncertainty is done using both the Guide to the expression of uncertainty in measurement method (GUM method) and Adaptive Monte Carlo method (AMCM method), and the evaluation results of GUM method are verified by AMCM method correspondingly, the effectiveness of this metering theory and the corresponding uncertainty evaluation methods are also illustrated by analyzing the length of coverage interval and numerical tolerances of measurement uncertainty. The research results show that the relative combined uncertainty of the metering system is not higher than 0.18% with the parameters given in this paper. INDEX TERMS Metering method, measurement uncertainty evaluation, quantity value propagation model, 6-DOF space, underwater acoustic positioning system.

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“…[7] The polar plots of broadband TL are shown to be anisotropic with internal waves and continental shelfbreak, and the PDFs tends to become wider. [8,9] The sound field depends on multiple environmental parameters, among which the TL is most sensitive to the sediment sound speed and the water column sound speed field. [10,11] Moreover, some methods such as the polynomial chaos expansions, the uncertainty matrix, and the field shifting approximation are developed to quantify or approximate the acoustic propagation uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Acoustic propagation uncertainty in internal wave environments using an ocean-acoustic joint model

Gao¹,

Xu²,

Li³

et al. 2023

Chinese Phys. B

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An ocean-acoustic joint model is developed for the study of acoustic propagation uncertainty in internal wave environments. The internal waves are numerically produced by tidal forcing over a continental slope using an ocean model. Three parameters (i.e., internal wave, source depth, and water depth) contribute to the dynamic waveguide environments, and result in stochastic sound fields. The sensitivity of the transmission loss (TL) to environment parameters, statistical characteristics of the TL variation, and the associated physical mechanisms are investigated by the Sobol sensitivity analysis method, the Monte Carlo sampling, and the coupled normal mode theory, respectively. The results show that the TL is most sensitive to the source depth in the near field, resulted from the initial amplitudes of higher-order modes; while in the middle and the far fields, the internal waves are responsible for more than 80% of the total acoustic propagation contribution. In addition, the standard deviation of the TL in the near field and the shallow layer is smaller than in the middle and far fields and the deep layer.

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Acoustic Propagation Uncertainty and Probabilistic Prediction of Sonar System Performance in the Southern East China Sea Continental Shelf and Shelfbreak Environments

Cited by 9 publications

References 13 publications

Experimentally-Based Ocean Acoustic Propagation and Coherence Studies

Experimentally-Based Ocean Acoustic Propagation and Coherence Studies

Metering Method and Measurement Uncertainty Evaluation of Underwater Positioning System in Six Degrees of Freedom Space

Acoustic propagation uncertainty in internal wave environments using an ocean-acoustic joint model

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