%0 Journal Article
%A 崔哲
%A 邓洪伟
%A 傅莉
%T Research on statistical characteristics of engine backward
RCS based on K-KDE algorithm
%D 2023
%R 10.19682/j.cnki.1005-8885.2023.2014
%J 中国邮电高校学报（英文）
%P 33-42
%V 30
%N 4
%X In order to solve the problem of low accuracy of traditional fixed window width kernel density estimation (KDE)
in radar cross section (RCS) statistical characteristics analysis, an improved Epanechnikov KDE (K-KDE)
algorithm was proposed to analyze the statistical characteristics of the engine's backward RCS. Firstly, the K-nearest
neighbor method was used to calculate the dynamic window width of the K-KDE, and the Euclidean distance of
each adjacent sample was used to judge the local density of the sample, and then the window width of the kernel
function was adjusted by the distance between the sample point and the nearest neighbor to complete the KDE.
Secondly, based on the K-KDE and the traditional KDE algorithm, the cumulative probability density function
(CPDF) of four RCS random distribution sample points subject to fixed parameters was calculated. The results
showed that the root mean square error of the K-KDE was reduced by 31.2%, 38.8%, 38.1% and 31.9%
respectively compared with the KDE. Finally, the K-KDE combined with the second generation statistical analysis
models were used to analyze the statistical characteristics of the engine backward RCS.
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2023.2014