An Overview of Sequential Bayesian Filtering in Ocean Acoustics

Yardim, Caglar; Michalopoulou, Zoi‐Heleni; Gerstoft, Peter

doi:10.1109/joe.2010.2098810

Cited by 93 publications

(57 citation statements)

References 92 publications

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“…We have explored incorporating Kalman and particle filter tracking techniques into the geoacoustic inversion problem [Yardim 2011a, 2011b, Michalopoulou 2012. This enables spatial and temporal tracking of environmental parameters and their underlying probability densities, making geoacoustic tracking a natural extension to geoacoustic inversion techniques.…”

Section: Work Completedmentioning

confidence: 99%

Sequential Geoacoustic Filtering and Utilizing Ambient Noise for Geoacoustic Inversion

Gerstoft¹,

Yardim²,

Menon³

et al. 2013

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The development of new geoacoustic inversion methods, their use in the analysis of shallow water experimental data, and evaluation of geoacoustic model and parameter uncertainties including the mapping of these uncertainties through to system performance uncertainties. OBJECTIVESAnalysis of geoacoustic inversion data collected from various experiments. Of specific technical interest are: (1) development of methods to track the environmental parameters using sequential filtering, (2) use of ambient noise for estimation of seafloor structure parameters, and (3) the development of new inversion methods for use into the kHz frequency regime. In an ONR Graduate Traineeship Awards we address using Random Matrix Theory in ocean acoustics. APPROACH Sequential filteringA common feature of inverse problems in ocean acoustics is that estimates of underlying physical parameters are extracted from measured acoustic data. Geoacoustic inversion has been approached in the same framework, estimating, in addition to source location, ocean environment parameters and their uncertainty. Often, those parameters evolve in time or space, with acoustic data arriving at consecutive steps. Information on parameter values and uncertainty at preceding steps can be invaluable for the determination of future estimates but is often ignored.Sequential Bayesian filtering, tying together information on parameter evolution, a physical model relating acoustic field measurements to the unknown quantities, and a statistical model describing random perturbations in the field observations, offers a framework for the solution of such problems.

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Section: Work Completedmentioning

confidence: 99%

Sequential Geoacoustic Filtering and Utilizing Ambient Noise for Geoacoustic Inversion

Gerstoft¹,

Yardim²,

Menon³

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…These include both the Kalman family of filters and sequential Monte Carlo methods known as particle filters (PF). 9,10 The PF gets its name from particles…”

Section: Passive Fathometer Particle Filteringmentioning

confidence: 99%

“…The filter not only tracks the model parameters but also the most suitable model itself. Multiple model estimation has been applied to spatial arrival time tracking 10,19 and multilayer geoacoustic tracking. 14 The spatial arrival time tracking approach, which forms the basis for this work, implements filtering based on importance sampling and tracks both amplitudes and arrival times, also integrating a model transition matrix.…”

Section: Passive Fathometer Particle Filteringmentioning

confidence: 99%

Particle filtering for passive fathometer tracking

Michalopoulou

Yardim

Gerstoft

2011

The Journal of the Acoustical Society of America

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Seabed interface depths and fathometer amplitudes are tracked for an unknown and changing number of sub-bottom reflectors. This is achieved by incorporating conventional and adaptive fathometer processors into sequential Monte Carlo methods for a moving vertical line array. Sediment layering information and time-varying fathometer response amplitudes are tracked by using a multiple model particle filter with an uncertain number of reflectors. Results are compared to a classical particle filter where the number of reflectors is considered to be known. Reflector tracking is demonstrated for both conventional and adaptive processing applied to the drifting array data from the Boundary 2003 experiment. The layering information is successfully tracked by the multiple model particle filter even for noisy fathometer outputs.

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“…For example, for a tracking application where the previous geoacoustic parameters and source location 12,13 already are calculated and there is a good prior information about the current values, a smaller search interval width 2Dr is needed. So it is possible to use a large Df and purposefully undersample the frequency response to reduce the computational cost without sacrificing performance.…”

Section: Selection Of the Frequency Sampling Intervalmentioning

confidence: 99%

Range aliasing in frequency coherent geoacoustic inversion

Yardim

Gerstoft

Hodgkiss

2011

The Journal of the Acoustical Society of America

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This paper discusses the effects of frequency selection on source localization and geoacoustic inversion methods that use frequency coherent objective functions. Matched-field processors based on frequency-coherent objective functions often have rapidly fluctuating range ambiguity surfaces. Insufficient sampling in frequency domain results in range aliasing terms that affect geoacoustic inversion. Range aliasing and its effects on source localization and environmental parameter inversion are demonstrated on data collected during the MAPEX2000 experiment. Guidance for frequency selection to avoid range aliasing is provided.

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An Overview of Sequential Bayesian Filtering in Ocean Acoustics

Cited by 93 publications

References 92 publications

Sequential Geoacoustic Filtering and Utilizing Ambient Noise for Geoacoustic Inversion

Sequential Geoacoustic Filtering and Utilizing Ambient Noise for Geoacoustic Inversion

Particle filtering for passive fathometer tracking

Range aliasing in frequency coherent geoacoustic inversion

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