Environmental inversion using dispersion tracking in a shallow water environment

Michalopoulou, Zoi‐Heleni; Aunsri, Nattapol

doi:10.1121/1.5026245

Cited by 24 publications

(2 citation statements)

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“…Both source localization and environmental inversion are crucial issues in underwater acoustics [1][2][3][4]. Matched-field processing (MFP) combines ocean acoustics and signal processing to solve the problem of passive source localization and/or environmental inversion, yielding excellent results [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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