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

doi:10.19682/j.cnki.1005-8885.2023.2014

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (4): 33-42.doi: 10.19682/j.cnki.1005-8885.2023.2014

• Artificial Intelligence • 上一篇下一篇

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

Fu Li, Cui Zhe, Deng Hongwei

1. School of Automation, Shenyang Aerospace University, Shenyang 110136, China 2. Avic Shenyang Engine Research Institute, Shenyang 110015, China

收稿日期:2022-09-01 修回日期:2023-06-28 接受日期:2023-08-31 出版日期:2023-08-31 发布日期:2023-08-31
通讯作者: Fu Li, E-mail: ffulli@163.com E-mail:ffulli@163.com

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

Fu Li, Cui Zhe, Deng Hongwei

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: ffulli@163.com E-mail:ffulli@163.com

摘要/Abstract

摘要： 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.

关键词: radar cross section, K-nearest neighbor method, kernel density estimation, fluctuation characteristics

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

Fu Li, Cui Zhe, Deng Hongwei. Research on statistical characteristics of engine backward RCS based on K-KDE algorithm[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(4): 33-42.

参考文献

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

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

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