Compressive acoustic sound speed profile estimation

Bianco, Michael J.; Gerstoft, Peter

doi:10.1121/1.4943784

Cited by 39 publications

(26 citation statements)

References 17 publications

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“…The previous studies indicate that the inversion of SSP has a good agreement with the measured data, in both shallow [8] and deep ocean [9]. Besides, the compressive estimation of SSP using EOFs is demonstrated in reference [10]. These characteristics of the spatiotemporal variability can be transformed to an estimating problem.…”

Section: Introductionmentioning

confidence: 86%

Joint Inversion for Sound Speed Field and Moving Source Localization in Shallow Water

Dai

Yang

2019

JMSE

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This paper develops a joint approach for time-evolving sound speed field (SSF) inversion and moving source localization in shallow water environment. The SSF is parameterized in terms of the first three empirical orthogonal function (EOF) coefficients. The approach treats both first three EOF coefficients and source parameters (e.g., source depth, range and speed) as state vectors of evolving with time, and a measurement vector that incorporates acoustic information via a vertical line array (VLA), and then the inversion problem is formulated in a state-space model. The processors of the extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) are used to estimate the evolution of those six parameters. Simulation results verify the proposed approach, which enable it to invert the SSF and locate the moving source simultaneously. The root-mean-square-error (RMSE) is employed to evaluate the effectiveness of this proposed approach. The interfile comparison shows that the EnKF outperform the EKF. For the EnKF, the robustness of the approach under the sparse vertical array configuration is verified. Moreover, the impact of the source-VLA deployment on the estimation is also concerned. formulated the evolution of environmental parameters by extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF), which converted geoacoustic inversion into tracking techniques [16]. Carrière put forward a state-space model for acoustic measurement data assimilation problem, and inverted the SSF by EnKF firstly [17]. Reference [18] proposed an improved algorithm compared with the performance of PF and EnKF to track the SSP, the improved filter had a better accuracy but computational complexity was greatly increased. However, the above methods have not considered source state, and it employed a traditional fixed source-receiver system, which has a lower spatial resolution. The geoacoustic characterizations of wide areas through inversion require easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles [19]. By employing a moving source launch signal to get high resolution, the information of the acoustic field is increasing. Afterwards, Dosso et al. examined the motion-compensated acoustic localization, which performed much better than static-model localization method or a localization based on applying fixed travel-time corrections [20]. In addition, based on the theory of compressed sensing, several algorithms are applied to direction-of-arrival (DOA) tracking, Das formulated the tracking problem of recovering a low-rank matrix and a sparse matrix by considering all snapshots together, rather than estimating the DOA snapshot-by-snapshot [21]. Guiding by the above-mentioned, our motivation and interest in this paper is proposing a joint scheme to reconstruct the SSF and locate the source simultaneously, rather than carrying out in separate inversion steps.To perform the inversion, source parameters (such as source location) should be taken into consi...

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Section: Introductionmentioning

confidence: 86%

Joint Inversion for Sound Speed Field and Moving Source Localization in Shallow Water

Dai

Yang

2019

JMSE

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“…It is crucial to estimate the sound speed profile (SSP) of a water column to reliably locate a source or to conduct geoacoustic inversion [1][2][3][4][5]. Many studies have used acoustic measurement data to infer the SSP (i.e., acoustic tomography) [6][7][8][9][10]. Originally, ocean acoustic tomography is conducted using travel times of arrivals measured at a single hydrophone [6].…”

Section: Introductionmentioning

confidence: 99%

“…A is a sensing matrix that shows the linear relation between x and y, and these are defined differently according to the given problem. In underwater acoustics, CS has been applied to obtain a high-resolution DOA with a limited number of sensors in an array [14][15][16][17][18], and the applicability of CS has been expanded to source localization [19,20] and ocean parameter inversion [10,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, CS has been applied to obtain the SSP represented as a linear combination of shape functions in the SSP dictionary, with a sparsity-promoting objective function used to exploit the sparsity of the solution after constructing a linear relation between the (given) measurement and (unknown) parameter [10]. The measurement and the parameter to be recovered in the compressive SSP estimation are the acoustic fields at the sensors of a vertical line array (VLA) and the shape function coefficients, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Compressive Sound Speed Profile Inversion Using Beamforming Results

Choo

Seong

2018

Remote Sensing

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Sound speed profile (SSP) significantly affects acoustic propagation in the ocean. In this work, the SSP is inverted using compressive sensing (CS) combined with beamforming to indicate the direction of arrivals (DOAs). The travel times and the positions of the arrivals can be approximately linearized using their Taylor expansion with the shape function coefficients that parameterize the SSP. The linear relation between the travel times/positions and the shape function coefficients enables CS to reconstruct the SSP. The conventional objective function in CS is modified to simultaneously exploit the information from the travel times and positions. The SSP is estimated using CS with beamforming of ray arrivals in the SWellEx-96 experimental environment, and the performance is evaluated using the correlation coefficient and mean squared error (MSE) between the true and recovered SSPs, respectively. Five hundred synthetic SSPs were generated by randomly choosing the SSP dictionary components, and more than 80 percent of all the cases have correlation coefficients over 0.7 and MSE along depth is insignificant except near the sea surface, which shows the validity of the proposed method.

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“…Here the variation of the salinity degree in ocean water leads to nonstraight propagation paths and multiple reflections at the water surface [3][4][5][6]. Are similar effects possible also for outdoor sound propagation and how would TDOA measurements be affected?…”

Section: Introductionmentioning

confidence: 99%

Acoustic Source Localization under Variable Speed of Sound Conditions

Rabenstein

Annibale

2017

Wireless Communications and Mobile Computing

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The rich literature on acoustic source localization mostly relies on the assumption of a constant value for the speed of sound. This hypothesis allows establishing simple relations between range differences and time differences and leads to effective estimation algorithms. However, it must be challenged for certain applications of wireless acoustic sensor networks in multizone buildings and outdoor environments. This article revisits the source localization problem for the more general case of an unknown value for the speed of sound. It reviews the physical foundations for the dependence of the speed of sound on the air temperature and presents the essential approaches to acoustic source localization. On this basis, several methods for source localization under uncertain or variable speed of sound conditions from the literature are discussed. Applications from different fields are shown. They comprise the localization of sources, sensors, and reflecting surfaces, time-difference-of-arrival disambiguation, and the direct determination of the speed of sound or the air temperature from acoustic measurements.

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Compressive acoustic sound speed profile estimation

Cited by 39 publications

References 17 publications

Joint Inversion for Sound Speed Field and Moving Source Localization in Shallow Water

Joint Inversion for Sound Speed Field and Moving Source Localization in Shallow Water

Compressive Sound Speed Profile Inversion Using Beamforming Results

Acoustic Source Localization under Variable Speed of Sound Conditions

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