Multi-frequency analysis of Gaussian process modelling for aperiodic RCS responses of a parameterised aircraft model

摘要

Radar cross-section (RCS) of an object is a complex function of various geometric variables, frequency and angles of incidence. In this work, an artificial intelligence solution is provided to predict the non-deterministic characteristics of RCS using the supervised machine learning algorithm that involves Gaussian process (GP) regression. A parametrised aircraft model is used to generate training data where five variables are selected as predictors while the response is chosen to be monostatic RCS in the azimuth plane. To provide a comparison of GP modelling-based predictions, shooting and bouncing rays-based multi-frequency RCS simulations are used and the results show good agreement. To further validate the GP-based modelling approach, the data of a design point is compared with the measured RCS of 1:8 scaled-down aircraft model, which confirms the accuracy of the proposed methodology. Good prediction capabilities of GP regression for RCS evaluation of complex geometries and requirement of small data set make it an excellent tool for exploring the large design space as well as integration into multi-disciplinary design optimisation environments.