Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Bayesian focalization: quantifying source localization with environmental uncertainty.

Stan E Dosso1, Michael J Wilmut

  • 1School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. sdosso@uvic.ca

The Journal of the Acoustical Society of America
|June 7, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Parallel tempering in trans-dimensional Bayesian geoacoustic inversion for high-information-content data and multi-parameter models.

JASA express letters·2025
Same author

Comparison and combination of matched-field and modal-dispersion inversion for seabed geoacoustic profiles at the New England Mud Patch

The Journal of the Acoustical Society of America·2025
Same author

Bayesian matched-field inversion for shear and compressional geoacoustic profiles at the New England Mud Patcha).

JASA express letters·2025
Same author

Trans-dimensional inversion for seafloor properties for three mud depocenters on the New England shelf under dynamical oceanographic conditionsa).

The Journal of the Acoustical Society of America·2024
Same author

Depth and frequency dependence of geoacoustic properties on the New England Mud Patch from reflection coefficient inversiona).

The Journal of the Acoustical Society of America·2023
Same author

Joint trans-dimensional inversion for water-column sound speed and seabed geoacoustic models.

JASA express letters·2023
Same journal

High-resolution depth estimation for multiple wideband sources in deep sea via sparse Bayesian learninga).

The Journal of the Acoustical Society of America·2026
Same journal

Depression markers in speech: An approach based on tract variables dynamics.

The Journal of the Acoustical Society of America·2026
Same journal

The oyster toadfish (Opsanus tau) alters active and diurnal calling amid vessel noise in New York City.

The Journal of the Acoustical Society of America·2026
Same journal

Experimental noise characterisation of phase-locked tandem-rotor in edgewise flight.

The Journal of the Acoustical Society of America·2026
Same journal

The tune-text-temporal synergy: Prosodic effects of final segmental weakening in Neapolitan.

The Journal of the Acoustical Society of America·2026
Same journal

Monitoring vessel movement above critical offshore infrastructure using distributed acoustic sensing.

The Journal of the Acoustical Society of America·2026
See all related articles

Accurate ocean acoustic source localization requires balancing environmental uncertainties with data quality. Reliable results depend on sufficient prior environmental knowledge and robust data information content, such as signal-to-noise ratio and multiple frequencies.

Area of Science:

  • Ocean acoustics
  • Geophysics
  • Signal processing

Background:

  • Ocean acoustic source localization is crucial for underwater applications.
  • Environmental uncertainties (water column, seabed) and data limitations (SNR, frequencies) significantly impact localization accuracy.
  • Previous methods often struggle to adequately quantify localization uncertainty under environmental variability.

Purpose of the Study:

  • To apply a Bayesian formulation for quantifying ocean acoustic source localization uncertainty.
  • To investigate the influence of environmental property uncertainty and data information content on localization accuracy.
  • To develop an efficient sampling method for complex environmental and source location parameter spaces.

Main Methods:

  • Utilized a Bayesian approach, extending the optimum uncertain field processor.

Related Experiment Videos

  • Employed Metropolis Gibbs' sampling for environmental parameters and heat-bath Gibbs' sampling for source location.
  • Applied the methodology to shallow-water acoustic data from the Mediterranean Sea.

    • Main Results:

    • Demonstrated that reliable localization necessitates a synergistic combination of prior environmental information and data information.
    • Showcased that low signal-to-noise ratio (SNR) single-frequency localization is only feasible with minimal environmental uncertainties.
    • Highlighted that substantial environmental uncertainties require higher SNR and/or multifrequency data for successful source localization.

    Conclusions:

    • The study confirms the critical interplay between environmental knowledge and data quality for accurate acoustic source localization.
    • Effective ocean acoustic localization is achievable by carefully managing uncertainties in both environmental properties and observational data.
    • The developed Bayesian framework provides a robust method for assessing and improving localization performance in complex underwater environments.