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Passive source localization based on multipath arrival angles with a vertical line array using sparse Bayesian

Yubo Qi1, Shihong Zhou1, Zailei Luo2

  • 1State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.

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This summary is machine-generated.

This study introduces a novel passive source localization method using a vertical line array. The technique accurately estimates source depth and location by analyzing direct and surface-reflected acoustic paths, even for narrowband sources.

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Area of Science:

  • Oceanography
  • Acoustics
  • Signal Processing

Background:

  • Traditional source localization in deep water relies on multipath time delays or frequency interference, often struggling with accurate depth estimation for narrowband sources at greater ranges.
  • Increasing bandwidth requirements with source range pose challenges for existing deep-water localization techniques.

Purpose of the Study:

  • To propose a passive source localization method using a vertical line array suitable for both broadband and narrowband sources.
  • To enable accurate depth estimation for sources in deep water by analyzing acoustic path variations.

Main Methods:

  • A passive localization method utilizing a vertical line array is developed, focusing on the variation trends of multipath angles with source range and depth.
  • Source localization is achieved by matching measured direct and surface-reflected path angles with model-based values on a predefined grid.
  • Sparse Bayesian learning is employed to resolve and estimate multipath angles, outperforming conventional and minimum-variance distortionless-response beamforming.

Main Results:

  • The proposed method demonstrates effective source localization and depth estimation using direct and surface-reflected acoustic paths.
  • Sparse Bayesian learning shows superior performance in resolving multipath angles compared to conventional beamforming techniques.
  • Simulations and experimental data from explosive sources in the South China Sea validate the method's performance.

Conclusions:

  • The developed passive source localization method offers a robust solution for deep-water environments, applicable to both broadband and narrowband sources.
  • The technique effectively overcomes limitations in depth estimation for remote narrowband sources.
  • The use of sparse Bayesian learning enhances multipath angle estimation accuracy, crucial for precise localization.