Sequential Inversion for Geoacoustic Parameters in the South China Sea Using Modal Dispersion Curves

Guo, Xuhong; Yang, Kunde; Duan, Rui; Ma, Yuanliang

doi:10.1007/s40857-017-0082-y

Cited by 7 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The state vector x k contains the geoacoustic parameters at frame k. In this paper, f (•) is simplified as the unit matrix I due to the lack of enough prior information about the change in geoacoustic parameters. The measurement vector y k contains the modal dispersion curves which are extracted from the time-frequency diagram of the received signal with warping transform and described as a set of travel time at different propagation modes and different frequencies [7]. The state noise vector w k and measurement noise vector v k can be modeled as covariance matrices Q k and R k , respectively.…”

Section: Sequential Estimation Methodsmentioning

confidence: 99%

“…In shallow ocean environments, the modal dispersion phenomenon can be observed, and its shape is strongly influenced by geoacoustic parameters. The fact that only one hydrophone is needed to observe this phenomenon reduces the experimental cost and the complexity of recording systems [6,7]. Compared with inversion methods that require high signal-to-noise ration (SNR) and complex signal processing techniques, modal dispersion curves can be extracted from the environment with unknown parameters and strong noise by using warping transform [8], making this method especially suitable for wide-band signals (air-gun or explosive charges) received by one hydrophone [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results

Liu

Yang

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

An adaptive particle filter method is presented for performing sequential geoacoustic estimation of a shallow water acoustic environment using the explosive sound sources. This approach treats environmental parameters and source–receiver distance as unknown random variables that evolve as the source moves. As a sequential estimation method, this approach reduces the expense of computation than genetic algorithm and yields results with the same accuracy. Comparing with standard Particle filter, proposed method can adjust control parameters to adapt to a rapidly changing environment. This approach is demonstrated on the shallow water sound propagation data which was collected during the ASIAEX 2001 experiment. The results indicate that the geoacoustic parameters are well estimated and source–receiver distance are also well determined.

show abstract

Section: Sequential Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results

Liu

Yang

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Numerous approaches have been developed to invert the sound speed in the water volume fast and accurate. To reduce the degrees of freedom, ocean parameters include the SSP are often represented in terms of EOFs [6,7]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Dai

Yang

2019

JMSE

View full text Add to dashboard Cite

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...

show abstract

Application of Dual-Source Modal Dispersion and Variational Bayesian Monte Carlo Method for Local Geoacoustic Inversion in Weakly Range-Dependent Shallow Water

2022

View full text Add to dashboard Cite

Sequential Inversion for Geoacoustic Parameters in the South China Sea Using Modal Dispersion Curves

Cited by 7 publications

References 30 publications

Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results

Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results

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

Application of Dual-Source Modal Dispersion and Variational Bayesian Monte Carlo Method for Local Geoacoustic Inversion in Weakly Range-Dependent Shallow Water

Contact Info

Product

Resources

About