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

Abstract

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...

An Improved Particle Filtering Technique for Source Localization and Sound Speed Field Inversion in Shallow Water

“…To reduce the degrees of freedom, the SSP is often represented in terms of EOFs [10]. In a previous paper, we verified the inversion of SSP has a good agreement with the measured data [11]. The discrete adjacent SSPs in each time interval are combined to form the SSF in the whole period.…”

“…In practice, the number of EOFs representation can be reduced. It is known that the first three EOFs are capable of representing SSP [11], the second block is given by…”

“…Sequential Bayesian filters such as extended Kalman [14], ensemble Kalman [11,15], unscented Kalman [16], and PF [17][18][19] have been used successfully for ocean acoustic applications in previous studies. An excellent overview has described that the sequential Bayesian filtering provide a suitable framework for estimating and updating the parameters of a system as data become available, examples include target localization, spatial arrival time tracking, and geoacoustic inversion [20].…”

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

“…In practice, the number of EOF representations can be reduced. In our previous work, it was shown that the first three EOFs are capable of representing SSP [23]. The second block is given by:…”