RCS Distribution Fitting using Improved Nonparametric Model

Abstract

An accurately established model of radar cross section (RCS) distribution is significant to target detection. Aimed at the problems that typical fluctuation models have inadequate accuracy in characterizing the RCS distribution to real targets, and the nonparametric model in forms of Legendre polynomials performs well but is in difficulty of giving the optimal order, an improved nonparametric model is proposed and verified in this work. Firstly, some deficiencies to determination of polynomial order in the nonparametric model are pointed out. And then, a dichotomous finite comparison method is presented to quickly search the optimal model order. Finally, some RCS measurements are analyzed while the fitting errors and computational complexity of several models to RCS distribution fitting are compared, together with the Kolmogorov-Smirnov test for goodness of fit. Data results show that the improved nonparametric model has good fitting effect and moderate computational complexity for RCS distribution.