DAVMAS-GP: Domain aware variance minimizing Gaussian process regression for complex monostatic RCS prediction
Jao, Kenneth
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Permalink
https://hdl.handle.net/2142/125760
Description
Title
DAVMAS-GP: Domain aware variance minimizing Gaussian process regression for complex monostatic RCS prediction
Author(s)
Jao, Kenneth
Issue Date
2024-07-19
Director of Research (if dissertation) or Advisor (if thesis)
Peng, Zhen
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Computational electromagnetics
covariance function
Gaussian process regression (GPR)
surrogate model
nonstationary kernel
variance minimization
adaptive sampler
radar cross section (RCS)
Abstract
We present our method DAVMAS-GP, a Domain Aware Variance Minimizing Adaptive Sampling Gaussian Process for the prediction of RCS characteristics, including the complex vertically (VV) and horizontally (HH) polarized scattered far-field in both the angular and frequency domains. The method uses Gaussian process regression at its core, but employs the usage of nonstationary kernels, and our adaptive sampler VMAS to attain high accuracy predictions under extremely sparse sampling conditions. We validate our method with an aircraft model which exhibits complex scattering phenomena. Numerical results show that DAVMAS-GP is able to reduce the predictive root-mean-square-error (RMSE) by at least 98.5% compared to traditional methods of combining a Matérn kernel with non-informed Latin Hypercube sampling (LHS). With VMAS, < 1% RMSE is achieved using only 2% of samples. Allowing up to 4% of samples enables < 0.05% RMSE, across all vertical and horizontal complex components.
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