RCS Uncertainty Quantification Using the Feature Selective Validation Method

Abstract

Uncertainty quantification is an important issue in the field of radar cross section (RCS) research. To quantify the impact of specific uncertainty factor on RCS, a novel approach based on the feature selective validation (FSV) method combined with Monte Carlo (MC) method is proposed in this paper. MC method is applied as the basic framework for uncertainty analysis, and FSV is initially employed to compare the results derived from sufficient uncertainty simulations. To facilitate and enhance the massive data assessment, a novel single and direct indicator of FSV is proposed as a quantitative descriptor of data uncertainty. The feasibility of the proposed method in RCS uncertainty quantification is benchmarked through many RCS evaluation examples. The impact of attitude uncertainty on the target RCS, including the scene of dynamic flight, is also studied by the proposed method.