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Gaussian processes (GPs) enhance matched-field processing (MFP) localization by improving signal accuracy. This study proposes a minimum mean squared error criterion to evaluate GP performance, aiding in optimal hyperparameter selection for better localization outcomes.

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Geophysics

Background:

  • Gaussian processes (GPs) are utilized for signal interpolation and denoising.
  • Matched-field processing (MFP) is a technique used for signal localization.
  • GPs have shown potential in enhancing MFP localization accuracy and robustness.

Purpose of the Study:

  • To propose a minimum mean squared error (MMSE) criterion for evaluating GP interpolation performance in MFP.
  • To compare the performance of different kernel and likelihood functions for GPs in MFP.
  • To identify optimal hyperparameters and assess challenges in data-limited scenarios for GPs-MFP.

Main Methods:

  • Utilizing a known true field to evaluate GP interpolation.
  • Applying the MMSE criterion to quantify performance.
  • Comparing various kernel and likelihood functions within the GP framework.
  • Analyzing interpolation and localization results under different conditions.

Main Results:

  • The MMSE criterion effectively evaluates GP interpolation and MFP localization performance.
  • Performance comparison of different GP components (kernels, likelihoods) is facilitated.
  • Optimal hyperparameters and interpolation strategies can be identified.
  • Challenges and performance upper bounds for GPs-MFP are highlighted, especially with limited data.

Conclusions:

  • The proposed MMSE criterion provides a robust method for assessing GP performance in MFP.
  • GP methods can significantly improve MFP localization, but performance is sensitive to data availability and model choice.
  • This work establishes a benchmark for GPs-MFP and guides future research in data-scarce environments.