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Beamforming using subspace estimation from a diagonally averaged sample covariance.

Jorge E Quijano1, Lisa M Zurk2

  • 1School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia V8P5C2, Canada.

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Summary
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This study introduces a new Toeplitz-constrained estimator for adaptive beamforming with large sonar arrays. The method improves target localization by enhancing signal detection in noisy environments with limited data.

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Area of Science:

  • Acoustics and Signal Processing
  • Array Signal Processing
  • Underwater Acoustics

Background:

  • Adaptive beamforming with large-aperture sonar arrays requires substantial data, often unavailable due to limited stationarity.
  • Existing methods struggle with target localization when data snapshots are scarce.

Purpose of the Study:

  • To develop a Toeplitz-constrained estimator for the clairvoyant signal covariance matrix.
  • To improve high-resolution target localization using limited data from large-aperture sonar arrays.

Main Methods:

  • Averaging sample covariance matrix subdiagonals and extrapolating using maximum entropy.
  • Constructing signal-subspace projector matrices from Toeplitz-constrained covariance eigenvectors.
  • Utilizing Monte Carlo simulations and experimental data for validation.

Main Results:

  • The proposed estimator effectively handles limited data snapshots common in large-aperture arrays.
  • Toeplitz-constrained eigenvectors yield improved signal-subspace projectors, reducing noise.
  • Enhanced detection of closely spaced targets is achieved through subspace beamforming.

Conclusions:

  • The Toeplitz-constrained estimator provides a robust solution for adaptive beamforming with limited data.
  • The method demonstrates superior performance over classic projectors, especially with increasing array aperture.
  • Validated by simulations and experimental data, this approach enhances sonar target localization capabilities.