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Joint towed array shape and direction of arrivals estimation using sparse Bayesian learning during maneuvering.

Zheng Zheng1, T C Yang1, Peter Gerstoft2

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.

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This study introduces an adaptive bow sparse Bayesian learning (ABSBL) algorithm to accurately estimate underwater towed array configurations and target directions. The method improves upon existing techniques, potentially eliminating the need for engineering sensors.

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Array processing

Background:

  • Large aperture towed arrays are crucial for detecting weak underwater targets.
  • Array configuration errors during maneuvering degrade beamformer performance.
  • Current methods for estimating array positions using sensors or acoustic sources are often inadequate.

Purpose of the Study:

  • To propose a novel algorithm, adaptive bow sparse Bayesian learning (ABSBL), for jointly estimating towed array configuration and signal directions of arrival (DOAs).
  • To address the limitations of existing methods in accurately determining array element positions during maneuvering.

Main Methods:

  • Developed an adaptive bow (AB) sparse Bayesian learning (SBL) algorithm (ABSBL).
  • Modeled the towed array's shape as a parabola during slow turns.
  • Treated the array bow as a hyperparameter within the SBL framework for joint estimation.
  • Utilized received acoustic data for estimation.

Main Results:

  • Simulations demonstrated ABSBL's accuracy in estimating array bow and target DOAs when turning direction is known.
  • Application to MAPEX2000 data showed agreement between ABSBL estimates and those from relative time delays.
  • ABSBL performance surpassed GPS-based methods using the water-pulley model.

Conclusions:

  • The ABSBL algorithm provides accurate joint estimation of array bow and DOAs from acoustic data.
  • This method offers a potential alternative to engineering sensors for array configuration estimation.
  • The approach shows promise for improving underwater acoustic detection systems.