Improved Gaussian Process Regression Inspired by Physical Optics for the Conducting Target's RCS Prediction

Abstract

In this letter, we propose an improved Gaussian process regression (GPR) to accurately predict the monostatic radar cross section of conducting targets as a function of the incident angle and frequency. Inspired by physical optics, we assume the covariance function as the sum of linear periodic covariance functions. Experiments involving the simulated and measured data are carried out to assess the proposed method. Results show that our method has better prediction performance than GPR with a local periodic covariance function, with a consistent reduction, up to 39% on simulated data and 43% on measured data, of the predictive root mean square error.