Size Prediction of Radar Target Based on Monostatic Radar Cross Section

Target size is an important indicator for reconstructing the contour of the radar target. Early accurate prediction of target size is beneficial for subsequent target evaluation. In our paper, we propose a method to predict the target size for radar target using machine learning based on its monostatic Radar Cross Section (RCS). Our prediction scheme comprises a deep neural network (DNN) model with a multi-hidden layer. The experimental results show that the proposed approach achieves 100% prediction accuracies for the height and radius values of the cylinder target.