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This study introduces passive aperture extension using sparse Bayesian learning for improved direction-of-arrival estimation. The method enhances accuracy and resolution, even without array element overlap, outperforming traditional techniques.

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Array Geometries

Background:

  • Passive synthetic aperture (PSA) extension enhances direction-of-arrival (DOA) estimation accuracy by creating a larger virtual aperture.
  • Traditional extended towed array measurement (ETAM) requires array element overlap for accurate phase factor estimation, failing with multiple sources if overlap is absent.

Purpose of the Study:

  • To propose a novel passive aperture extension method using sparse Bayesian learning (SBL) to overcome limitations of traditional ETAM.
  • To enable accurate DOA estimation even when array elements lack overlap during motion.

Main Methods:

  • Sparse Bayesian Learning (SBL) is employed to simultaneously estimate phase correction factors for multiple targets.
  • Phase compensation is applied to extended aperture manifold vectors for improved DOA estimation.
  • The proposed method integrates SBL with PSA extension for enhanced performance.

Main Results:

  • The proposed method successfully extends the passive synthetic aperture.
  • Achieved higher azimuth resolution and accuracy compared to conventional beamforming (CBF) and SBL without extension.
  • Demonstrated robust performance even with non-overlapped array elements, outperforming traditional ETAM.

Conclusions:

  • Passive aperture extension with sparse Bayesian learning offers a superior approach for DOA estimation.
  • The method effectively addresses the limitations of traditional ETAM, particularly in scenarios with sparse or no element overlap.
  • This technique provides enhanced accuracy and resolution for passive array systems.