The Journal of China Universities of Posts and Telecommunications ›› 2023, Vol. 30 ›› Issue (4): 33-42.doi: 10.19682/j.cnki.1005-8885.2023.2014

• Artificial intelligence • Previous Articles     Next Articles

Research on statistical characteristics of engine backward RCS based on K-KDE algorithm

Fu Li, Cui Zhe, Deng Hongwei   

  1. 1. School of Automation, Shenyang Aerospace University, Shenyang 110136, China 2. Avic Shenyang Engine Research Institute, Shenyang 110015, China
  • Received:2022-09-01 Revised:2023-06-28 Accepted:2023-08-31 Online:2023-08-31 Published:2023-08-31
  • Contact: Fu Li, E-mail:

Abstract: 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.

Key words: radar cross section, K-nearest neighbor method, kernel density estimation, fluctuation characteristics