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This study introduces the multitapered Min processor, enhancing coprime sensor arrays (CSAs) for underwater sonar. It reduces spatial power spectral density (PSD) variance without needing more data, improving passive beamforming performance.

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Underwater Acoustics

Background:

  • Coprime sensor arrays (CSAs) offer sparse geometry for improved spatial resolution.
  • Traditional CSA Min processors struggle with variance in nonstationary sonar environments due to insufficient snapshots.
  • The multitaper method can reduce power spectral density (PSD) variance by averaging uncorrelated estimates.

Purpose of the Study:

  • To propose the multitapered Min (MT-Min) processor for unambiguous PSD estimation in passive beamforming.
  • To analyze the statistical properties of the MT-Min processor's PSD estimates.
  • To mitigate spurious peaks in CSA-based PSD estimates.

Main Methods:

  • Derivation of the probability density function and moments for the MT-Min processor with white Gaussian processes.
  • Analytical derivation and simulation-based verification of variance reduction.
  • Extension of the multitaper method to a mixture of Min and Product processors with noise plateau normalization.

Main Results:

  • The MT-Min processor achieves desirable variance properties for PSD estimation.
  • Analytical predictions of variance reduction are verified through simulations for both white and non-white processes.
  • The extended multitaper method effectively attenuates spurious peaks in CSA PSD estimates.

Conclusions:

  • The proposed MT-Min processor offers a viable solution for improving PSD estimation variance in passive sonar beamforming.
  • The method is robust for both white and non-white spatial processes.
  • The multitaper approach enhances the reliability of CSA-based passive beamforming in complex environments.