RCS Prediction of a Target Based on the Machine Learning

This paper is based on the decision tree method in machine learning to predict the Radar Cross Section (RCS) of a complex target, where the original samples are simulated by using the Shooting and Bouncing Rays (SBR) method. The prediction method achieves good results and alleviates the problem of traditional RCS computation being time-consuming and expensive. A series of experimental analyses have been conducted on missile and ship targets to evaluate the accuracy and effectiveness of the RCS prediction model. The results demonstrate that the decision tree method based on machine learning is effective to predict the RCS of a target.