Sequential inversion of self-noise using adaptive particle filter in shallow water

Bo, Liankun; Xiong, Jie; Ma, Shaoping

doi:10.1121/1.5032195

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…Recently, an adaptive particle filter has been developed to try to solve this problem and was applied to geoacoustic inversion of a horizontal towed array [35]. Compared to typical sequential filters, it uses an adaptive method to update Q k online rather than an assumed diagonal matrix, which ensures tracking performance in the time-varying environment.…”

Section: Discussionmentioning

confidence: 99%

Sequential Geoacoustic Inversion Using an Improved Kalman Particle Filter

Liu

Yang

2020

JMSE

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Geoacoustic inversion is an efficient method to study the physical properties and structure of ocean bottom while sequential geoacoustic inversion is a challenging task due to the complexity and non-linearity of the underwater environment. In this paper, an ensemble Kalman Particle filter is described to address the sequential geoacoustic inversion problem of range-dependent environment in shallow water. This filter combines the advantages of Particle filter and ensemble Kalman filter so its ability of tracking dynamical geoacoustic parameters is improved. The proposed filtering method is demonstrated with simulated data in a changing oceanic environment and outperforms Particle filter and ensemble Kalman filter. This method is also tested in sea-trial data collected from a shallow-water experiment in the East China Sea. The inverted sound speed in sediment is consistent with in situ measurement and the error between transmission loss predicted by inverted parameters, and the experimental transmission loss is small.

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Section: Discussionmentioning

confidence: 99%

Sequential Geoacoustic Inversion Using an Improved Kalman Particle Filter

Liu

Yang

2020

JMSE

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“…The joint estimation of a geoacoustic model and its associated parameters has been achieved through a reversible jump Markov chain Monte Carlo (rjMCMC) methodology [7]. In this study, a transdimensional particle filter (TDPF) method is proposed, which can perform model selection by adding a birth death form to a traditional particle filter and can increase computation efficiency by calculating particle swarms in parallel [8,9]. The proposed method is first tested on seabed bottom loss (BL) data derived from ambient noise data recorded by a vertical line array (VLA).…”

Section: Introductionmentioning

confidence: 99%

Transdimensional particle filtering for geoacoustic inversion applied to vertical and horizontal arrays

Wu,

Ren,

et al. 2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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In this study, a transdimensional particle filtering method is proposed to simultaneously estimate a geoacoustic model and its associated parameters, whose computational efficiency is improved through a mechanism of parallel calculations. The proposed method is first applied to seabed bottom loss derived from vertical array data collected in the Yellow Sea in 2016. The thickness and number of estimated sediment layers are close to those obtained in previous studies. Moreover, particle filtering has an intrinsic advantage in sequential processing, making it a straightforward method for mapping range-dependent geoacoustic properties of a horizontal array towed from a moving ship. Range-dependent inversion is conducted for a spatial varying environment, and the data are collected from an experiment conducted in the South China Sea in 2022. The inversion results suggest that the sediment layer and associated geoacoustic parameters can be effectively tracked and that the thickness and number of estimated sediment layers are consistent with those obtained by reversible jump Markov chain Monte Carlo method.

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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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Sequential inversion of self-noise using adaptive particle filter in shallow water

Cited by 6 publications

References 40 publications

Sequential Geoacoustic Inversion Using an Improved Kalman Particle Filter

Sequential Geoacoustic Inversion Using an Improved Kalman Particle Filter

Transdimensional particle filtering for geoacoustic inversion applied to vertical and horizontal arrays

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

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