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This study introduces a Bayesian method for active sonar localization, improving accuracy despite challenging conditions like uncertain sound speed profiles. The approach effectively maps acoustic data to pinpoint object location and speed.

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Area of Science:

  • Ocean acoustics
  • Signal processing
  • Bayesian inference

Background:

  • Active sonar localization faces challenges including small receive apertures, uncertain sound speed profiles (SSP), and limited coherence time.
  • Accurate localization is critical for underwater object tracking and mapping.

Purpose of the Study:

  • To develop a computational Bayesian approach for robust active sonar localization.
  • To address challenges posed by uncertain sound speed profiles and limited acoustic data.
  • To characterize the scattered acoustic field and infer target location and speed.

Main Methods:

  • Inference on wavevectors from angle/Doppler spread arrivals to characterize the acoustic field.
  • Mapping wavevector posterior density to scattering body location and speed using eigenray interpolation and marginalization.
  • Modeling SSP uncertainty with a multivariate Gaussian and low-dimensional subspace modes.

Main Results:

  • The proposed Bayesian method effectively handles small receive apertures, uncertain SSPs, and limited coherence time.
  • Eigenray interpolation and marginalization successfully map wavevector information to target characteristics.
  • Demonstrated efficacy in a case study using Mediterranean Sea sound speed profiles.

Conclusions:

  • The computational Bayesian approach provides a robust solution for active sonar localization under challenging underwater conditions.
  • Accurate localization is achievable even with significant uncertainties in the sound speed profile.
  • The method offers a significant advancement in underwater acoustic sensing and target tracking.