Machine Learning-based Modeling and Uncertainty Quantification for Radar Cross Section of a Cone-like Target

Abstract

Radar cross section (RCS) plays an important role in the recognition of targets. RCS varies dramatically with the incident angle and the size of targets, and it is difficult to accurately predict the RCS values. In this paper, an efficient modeling and uncertainty quantification method based on the support vector machine and the k-nearest neighbor is proposed for the RCS prediction of cone-like targets. The proposed method is compared with two uncertainty quantification methods, an ensemble based on lower upper bound estimation and a neural network with dropout. The root mean square error, the prediction interval coverage probability, the mean prediction interval width and the computation time are used as the performance metrics, and the experimental results demonstrate that the proposed method is superior to the compared methods in accuracy and efficiency.