Design of radar radiation source identification method based on Random Convolution Kernel and XGboost

Shengnan Li; Jiajie Sun; Jingjing Guo; Yameng Niu; Boyun Liu

doi:10.1117/12.3011597

2 November 2023 Design of radar radiation source identification method based on Random Convolution Kernel and XGboost

Shengnan Li, Jiajie Sun, Jingjing Guo, Yameng Niu, Boyun Liu

Proceedings Volume 12919, International Conference on Electronic Materials and Information Engineering (EMIE 2023); 129190Y (2023) https://doi.org/10.1117/12.3011597
Event: 3rd International Conference on Electronic Materials and Information Engineering (EMIE 2023), 2023, Guangzhou,, China

Abstract

Aiming at the problems of low accuracy and poor timeliness of conventional radiation source recognition, a radar radiation source recognition method based on random convolution kernel and Stacking is proposed in this paper. The one-dimensional sequence radiation source signal is normalized, the random convolution check sequence signal is used for efficient feature extraction transformation, and the transformed feature data is trained and learned by XGboost model. Finally, the trained model is used to complete the recognition of radiation source. The experimental results show that this method has better timeliness and higher recognition rate than the traditional radiation source identification method, and has reference significance for engineering implementation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shengnan Li, Jiajie Sun, Jingjing Guo, Yameng Niu, and Boyun Liu "Design of radar radiation source identification method based on Random Convolution Kernel and XGboost", Proc. SPIE 12919, International Conference on Electronic Materials and Information Engineering (EMIE 2023), 129190Y (2 November 2023); https://doi.org/10.1117/12.3011597

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available