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Tracking of geoacoustic parameters using Kalman and particle filters
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10.1121/1.3050280
/content/asa/journal/jasa/125/2/10.1121/1.3050280
http://aip.metastore.ingenta.com/content/asa/journal/jasa/125/2/10.1121/1.3050280

Figures

Image of FIG. 1.
FIG. 1.

Geoacoustic tracking for three configurations: (a) Temporal tracking of the average, range-independent environment using a fixed-VLA-receiver and a towed-source, (b) spatial tracking of range-dependence using a towed-HLA-receiver and a towed-source, and (c) temporal tracking of the ocean sound speed profile for a fixed-VLA-receiver and a fixed-source.

Image of FIG. 2.
FIG. 2.

Seven-parameter geoacoustic model used in the simulations.

Image of FIG. 3.
FIG. 3.

Example 1: Comparison of the tracking algorithms: (a) Evolution of 100 different environments (Monte Carlo trajectories), and (b) RMS errors for the EKF, UKF, 200-point PF, and 2000-point PF obtained from tracking each of these 100 trajectories along with the theoretical lower limit for the RMS error, the square root of the posterior CRLB.

Image of FIG. 4.
FIG. 4.

Example 1: Performance improvement of PF as a function of number of particles expressed in terms of (a) filter efficiency, and (b) improvement over the EKF. The dashed line shows the attainable improvement limit.

Image of FIG. 5.
FIG. 5.

Example 2: Normalized objective functions for three different frequencies. The cost function for each parameter is obtained by fixing all other parameters to their true values (dashed line).

Image of FIG. 6.
FIG. 6.

Example 2: Tracking results of EKF, UKF, PF-200, and PF-5000 for the seven-parameter environment given in Fig. 1 using the long range VLA. True trajectories (dashed) are provided along with the tracking filter estimates (solid).

Image of FIG. 7.
FIG. 7.

Example 2: Evolution of the magnitude of the vertical acoustic field in the water column at the receiver array as the environment evolves in time. Hydrophone locations (circle) show the vertical sampling interval of the time-varying field.

Image of FIG. 8.
FIG. 8.

Example 2: Posterior probability density evolution for the sediment thickness for a 10000-point particle filter and the EKF: (a) Six snapshots at , 40, 84, 116, 152, and with the local particle histograms representing the PF distribution (solid) and the EKF Gaussian PDF (line) along with the true trajectory (dashed). (b) The continuous evolution of the PF PPD together with the local mean standard deviation of the EKF Gaussian.

Image of FIG. 9.
FIG. 9.

Example 3: Tracking results of EKF, UKF, PF-200, and PF-5000 for the seven-parameter environment given in Fig. 1 using the short range HLA configuration. True trajectories (dashed) are provided along with the tracking filter estimates (solid).

Tables

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TABLE I.

Environmental and simulation parameters used in example 1.

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TABLE II.

Performance comparison for example 1.

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TABLE III.

Simulation parameters for example 2.

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TABLE IV.

Results for example 2.

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TABLE V.

Simulation parameters and percent improvement of filters for example 3.

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/content/asa/journal/jasa/125/2/10.1121/1.3050280
2009-02-01
2014-04-19
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Tracking of geoacoustic parameters using Kalman and particle filters
http://aip.metastore.ingenta.com/content/asa/journal/jasa/125/2/10.1121/1.3050280
10.1121/1.3050280
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