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An eigenvector-based test for local stationarity applied to array processing.

Jorge E Quijano1, Lisa M Zurk2

  • 1School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Road, Victoria British Columbia V8P 5C2, Canada jorgeq@uvic.ca.

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Summary
This summary is machine-generated.

This study introduces an eigenvector method to segment sonar data, improving covariance matrix estimation for moving targets. This technique enhances the detection of quiet sources amidst interference using limited stationary data intervals.

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Area of Science:

  • Signal Processing
  • Underwater Acoustics
  • Array Processing

Background:

  • Estimating data covariance matrices in sonar array processing is difficult due to limited data stationarity with moving targets.
  • Accurate covariance matrix estimation is crucial for target detection and source localization in dynamic underwater environments.

Purpose of the Study:

  • To develop a data-driven method for segmenting sonar data into locally stationary intervals.
  • To provide improved bounds for snapshot computation in time-varying covariance matrix estimation.
  • To enhance the detection of weak targets in complex acoustic scenarios.

Main Methods:

  • An eigenvector-based approach is utilized for segmenting the data into locally stationary intervals.
  • The method determines data-driven upper bounds for the number of snapshots required for covariance matrix computation.
  • Simulated data from a horizontal array is used to validate the technique.

Main Results:

  • The eigenvector method effectively segments data, identifying intervals of local stationarity.
  • This segmentation provides higher, data-driven bounds for snapshot usage in covariance matrix calculations.
  • The application demonstrated successful detection of a quiet source despite a loud interferer.

Conclusions:

  • The proposed eigenvector-based segmentation method offers a robust solution for covariance matrix estimation in sonar array processing with moving targets.
  • This approach improves the reliability of target detection by maximizing the utility of available data snapshots within stationary intervals.
  • The technique shows promise for real-world applications in underwater acoustics, particularly for detecting low-power sources in noisy environments.