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

Li, Jianlong; Zhou, Hui

doi:10.1121/1.4790354

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…In an EKF, this is done by locally linearizing the state and measurement equations using the first order Taylor series expansion of the nonlinear transformations (such as the normal mode propagation model h(·)). After linearization, the state and measurement equations given in (17) and (18) to:…”

Section: Ekf Modelmentioning

confidence: 99%

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: Ekf Modelmentioning

confidence: 99%

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

Dai

Yang

2019

JMSE

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“…4 And the particle filter suffers from the well-known problem of sample degeneracy, and a large number of particles are often required. In the paper by Li et al, 5 the particle filter and the ensemble Kalman filter are combined to track the time-evolving sound speed profile. However, the performance is enhanced at the expense of tremendous computational cost.…”

Section: Introductionmentioning

confidence: 99%

A method for tracking time-evolving sound speed profiles using Kalman filters

Huang

2014

The Journal of the Acoustical Society of America

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This paper implements a weighted ensemble Kalman filter for tracking time-evolving sound speed profiles. The approach first updates the particles following the procedure of the ensemble Kalman filter and then resamples the updated particles according to their importance weights. The weights are evaluated by the classical geoacoustic inversion likelihood obtained from the Bartlett power objective functions. Example illustrates that the new method outperforms the ensemble with a comparable computational load.

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“…Once a tracking problem is defined as a state-space model with appropriate state and measurement equations, a suitable filter must be identified. Tracking filters, for example, the Kalman Filter family, PFs, and their extensions, have been successfully used in various tasks, such as source localization and tracking [13][14][15], environmental parameter estimation [4,16,17], geo-acoustic inversion [18][19][20], and spatial arrival time tracking [21,22]. These sequential Bayesian filters combine information on the evolution of parameters, functions that relate acoustic measurements to unknown quantities, and statistical models of random perturbations in the measurements [19].…”

Section: Introductionmentioning

confidence: 99%

Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

Dai

et al. 2021

JMSE

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Shallow water is a complex sound propagation medium, which is affected by the varying spatial–temporal ocean environment. Taking this complexity into account, the classical processing techniques of source localization and environmental inversion may be improved. In this work, a joint tracking approach for the moving source and environmental parameters of the range-dependent and time-evolving environment in shallow water is presented. The tracking scheme treats both the source parameters (e.g., source depth, range, and speed) and the environmental parameters (e.g., water column sound speed profile (SSP) and sediment parameters) at the source location as unknown variables that evolve as the source moves. To counter sample impoverishment and robustly characterize the evolution of the parameters, an improved particle filter (PF), which is an extension of the standard PF, is proposed. Two examples with simulated data in a slowly changing environment and experimental data collected during the ASIAEX experiment are utilized to demonstrate the effectiveness of the joint approach. The results show that we were able to track the source and environmental parameters simultaneously, and the uncertainties were evaluated in the form of time-evolving posterior probability densities (PPDs). The performance comparison confirms that the improved PF is superior to the standard PF, as it can reduce the parameter uncertainties. The tracking capabilities of the improved PF were verified with high accuracy in real-time source localization and well-estimated rapidly varying parameters. Moreover, the influence of different particle numbers on the improved PF tracking performance is also illustrated.

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

Cited by 14 publications

References 41 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

A method for tracking time-evolving sound speed profiles using Kalman filters

Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

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