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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 177921-177931
Author(s):  
Miao Dai ◽  
Ya'an Li ◽  
Jinying Ye ◽  
Kunde Yang
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Particle Filtering ◽  
Sound Speed ◽  
Filtering Technique ◽  
Field Inversion

Influence of Sound Speed Profile on Source Localization at Different Depths

2017 ◽  
Vol 25 (02) ◽  
pp. 1750026 ◽  
Author(s):  
L. Su ◽  
L. Ma ◽  
S. M. Guo
Keyword(s):  
Shallow Water ◽  
Normal Mode ◽  
Source Localization ◽  
Sound Speed ◽  
Speed Profile ◽  
Sound Speed Profile ◽  
Different Depths ◽  
Negative Gradient

The effect of sound speed profile (SSP) mismatch on source localization in shallow-water waveguides with a typical negative gradient (or thermocline) is studied numerically and experimentally. The results are interpreted using a normal mode model and a ray model. It is found that a matched-field processor is insensitive to SSP mismatch for sources above the thermocline. In addition, the sensitivity of the processor to SSP mismatch increases with the depth of sources above the thermocline.

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

2019 ◽  
Vol 7 (9) ◽  
pp. 295 ◽  
Author(s):  
Dai ◽  
Li ◽  
Yang
Keyword(s):  
Kalman Filter ◽  
Shallow Water ◽  
Source Localization ◽  
Sound Speed ◽  
Joint Inversion ◽  
Source Parameters ◽  
Water Environment ◽  
Depth Range ◽  
Moving Source ◽  
The Impact

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.

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

2021 ◽  
Vol 9 (11) ◽  
pp. 1203
Author(s):  
Miao Dai ◽  
Yaan Li ◽  
Jinying Ye ◽  
Kunde Yang
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Particle Filtering ◽  
Source Parameters ◽  
Simulated Data ◽  
Environmental Parameters ◽  
Performance Comparison ◽  
Depth Range ◽  
Parameter Uncertainties ◽  
Joint Tracking

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.

scholarly journals Measuring the Impact of Observations on the Predictability of the Kuroshio Extension in a Shallow-Water Model

2012 ◽  
Vol 42 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Werner Kramer ◽  
Henk A. Dijkstra ◽  
Stefano Pierini ◽  
Peter Jan van Leeuwen
Keyword(s):  
Shallow Water ◽  
Particle Filtering ◽  
Predictive Power ◽  
Kuroshio Extension ◽  
Water Model ◽  
Ensemble Prediction ◽  
Shallow Water Model ◽  
One Year ◽  
The Impact ◽  
The Kuroshio

Abstract In this paper, sequential importance sampling is used to assess the impact of observations on an ensemble prediction for the decadal path transitions of the Kuroshio Extension. This particle-filtering approach gives access to the probability density of the state vector, which allows the predictive power—an entropy-based measure—of the ensemble prediction to be determined. The proposed setup makes use of an ensemble that, at each time, samples the climatological probability distribution. Then, in a postprocessing step, the impact of different sets of observations is measured by the increase in predictive power of the ensemble over the climatological signal during one year. The method is applied in an identical-twin experiment for the Kuroshio Extension using a reduced-gravity shallow-water model. This study investigates the impact of assimilating velocity observations from different locations during the elongated and the contracted meandering states of the Kuroshio Extension. Optimal observation locations correspond to regions with strong potential vorticity gradients. For the elongated state the optimal location is in the first meander of the Kuroshio Extension. During the contracted state it is located south of Japan, where the Kuroshio separates from the coast.

Sign in / Sign up

Export Citation Format

Share Document