Dynamic Estimation of the Sound-Speed Profile from Broadband Acoustic Measurements

Carrière, Olivier; Hermand, Jean-Pierre; Meyer, Matthias; Candy, James V.

doi:10.1109/oceanse.2007.4302430

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Recently, the use of sequential Bayesian filtering methods involving a numerical acoustic propagation scheme has been proposed for tomography or geoacoustic inversion. [19][20][21][22] Sequential algorithms are particularly suitable when considering source and receiver in relative motion, continuous or long-term monitoring, and rangedependent environments. Moreover, they enable an optimal integration of the a priori knowledge of the variability of the inverted parameters and implicitly provide estimates of the uncertainty associated with the estimates (or even their probability density functions, when using advanced filtering algorithms).…”

Section: Introductionmentioning

confidence: 99%

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration

Carrière

Hermand

2012

The Journal of the Acoustical Society of America

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Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.

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Section: Introductionmentioning

confidence: 99%

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration

Carrière

Hermand

2012

The Journal of the Acoustical Society of America

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“…Tracking filters such as extended Kalman, 1, [14][15][16][17][18] unscented Kalman, 1,19 ensemble Kalman filter, 20 and particle filters 1, 21 have been used successfully for ocean acoustics. Previous geoacoustic inversion studies have shown that a geoacoustic particle filter ͑PF͒ is versatile and robust in tracking environmental changes in ocean acoustic applications.…”

Section: Introductionmentioning

confidence: 99%

Geoacoustic and source tracking using particle filtering: Experimental results

Yardim

Gerstoft

Hodgkiss

2010

The Journal of the Acoustical Society of America

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A particle filtering (PF) approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source. This approach treats both the environmental parameters [e.g., water column sound speed profile (SSP), water depth, sediment and bottom parameters] at the source location and the source parameters (e.g., source depth, range and speed) as unknown random variables that evolve as the source moves. This allows real-time updating of the environment and accurate tracking of the moving source. As a sequential Monte Carlo technique that operates on nonlinear systems with non-Gaussian probability densities, the PF is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities. The approach is demonstrated on both simulated data in a shallow water environment with a sloping bottom and experimental data collected during the SWellEx-96 experiment.

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“…(4) In this equation, (5) and 'T' means transverse. Let the k-th step sounds as then the (k+1)-th step sounds are derived as (6) …”

Section: Solving Methodsmentioning

confidence: 99%

Layered model sound speed profile estimation

Shiba

2013

2013 MTS/IEEE OCEANS - Bergen

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The most important and the most difficult area is underwater for harbor protections. Acoustic sensors are applied for these areas, since visible lights and other sensors cannot reach hundreds of meters range. However, sound propagation is extremely complicated because of non-stationary and nonuniform refraction index distributions. Sound speed profiles should be measured or estimated before monitoring operations. Usually expendable sensors like BTs are used for temperature distribution measurements in the general sonar operations. But these devices are not suitable for frequent sound speed profile estimations for dynamical changing environments in harbors. A new concept using single sonar is introduced for sound speed profiles estimations in this presentation. This new approach is based on layered model. The procedure and the effects are presented with simulation results.

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Dynamic Estimation of the Sound-Speed Profile from Broadband Acoustic Measurements

Cited by 9 publications

References 14 publications

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration

Geoacoustic and source tracking using particle filtering: Experimental results

Layered model sound speed profile estimation

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