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.

中文翻译：

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高斯过程模型对参数化飞机模型非周期性RCS响应的多频分析

物体的雷达横截面（RCS）是各种几何变量，频率和入射角的复杂函数。在这项工作中，提供了一种人工智能解决方案，使用涉及高斯过程（GP）回归的监督机器学习算法来预测RCS的不确定性特征。参数化飞机模型用于生成训练数据，其中选择五个变量作为预测变量，同时选择响应作为方位平面中的单基地RCS。为了对基于GP建模的预测进行比较，使用了基于射击和弹跳射线的多频RCS仿真，结果显示出很好的一致性。为了进一步验证基于GP的建模方法，将设计点的数据与测量的1：8缩小飞机模型的RCS相比较，这证实了所提出方法的准确性。GP回归具有良好的预测能力，可用于复杂几何形状的RCS评估以及对小数据集的需求，使其成为探索大型设计空间以及集成到多学科设计优化环境中的绝佳工具。