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Direction-of-arrival estimation for weak underwater targets via flexible sparsity-aware modeling.

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This study introduces a new Bayesian model for underwater direction-of-arrival (DOA) estimation. The method enhances accuracy and resolution, particularly in low signal-to-noise ratio (SNR) conditions with limited data.

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Statistical modeling

Background:

  • Direction-of-arrival (DOA) estimation is crucial for underwater applications.
  • Low signal-to-noise ratio (SNR) and limited snapshots challenge traditional and learning-based DOA methods.
  • Existing techniques suffer from degraded accuracy and resolution in adverse conditions.

Purpose of the Study:

  • To develop an improved DOA estimation method for underwater acoustics.
  • To address performance limitations in low SNR and limited snapshot scenarios.
  • To leverage Bayesian probabilistic modeling for enhanced DOA estimation.

Main Methods:

  • Proposed a Bayesian probabilistic model utilizing the generalized hyperbolic distribution for sparsity promotion.
  • Derived closed-form inference updates via variational inference.
  • Introduced a modified marginal likelihood computation for an adaptive approach to mitigate grid mismatch.

Main Results:

  • The proposed method demonstrates superior performance in low SNR and limited snapshot regimes.
  • Validated through simulations and real-world SWellEx-96 Experiment data.
  • Achieved improved accuracy and resolution in DOA estimation.

Conclusions:

  • The novel Bayesian approach effectively enhances DOA estimation in challenging underwater environments.
  • The method offers a robust solution for low SNR and limited observation scenarios.
  • Significant improvements in DOA estimation accuracy and resolution were confirmed.