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Block sparse Bayesian learning for broadband mode extraction in shallow water from a vertical array.

Haiqiang Niu1, Peter Gerstoft2, Emma Ozanich2

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|July 3, 2020
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Summary
This summary is machine-generated.

Block sparse Bayesian learning (BSBL) accurately estimates horizontal wavenumbers and modal depth functions for underwater acoustics. This method precisely retrieves acoustic properties without needing prior information about the environment or source.

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Array signal processing

Background:

  • Shallow-water waveguides present challenges for acoustic signal analysis.
  • Estimating horizontal wavenumbers and modal depth functions is crucial for understanding acoustic propagation.

Purpose of the Study:

  • To develop a robust method for estimating horizontal wavenumbers and modal depth functions using broadband signals.
  • To apply block sparse Bayesian learning (BSBL) for improved acoustic parameter retrieval in shallow water.

Main Methods:

  • Utilizing block sparse Bayesian learning (BSBL) for signal processing.
  • Constructing a dictionary matrix with multi-frequency modal depth functions and horizontal wavenumbers.
  • Leveraging the dispersion relation for dictionary generation.

Main Results:

  • High-precision retrieval of horizontal wavenumbers and modal depth functions was achieved.
  • The BSBL approach demonstrated effectiveness with block sparsity constraints.
  • Accurate estimations were obtained without requiring prior knowledge of sea bottom, moving sources, or source locations.

Conclusions:

  • BSBL is a powerful tool for acoustic parameter estimation in shallow-water environments.
  • The method shows promise for both simulated and experimental acoustic data analysis.
  • This technique advances the field of underwater acoustic signal processing.