Research for Radar Signal Classification Model Using Deep Learning Technique

Kim, Yongjun; Yu, Kihun; Han, Jinwoo

doi:2019.22.2.170

J. KIMS Technol > Volume 22(2); 2019 > Article

Research Paper

Journal of the Korea Institute of Military Science and Technology 2019;22(2):170-178.
DOI: https://doi.org/10.9766/KIMST.2019.22.2.170

Research for Radar Signal Classification Model Using Deep Learning Technique

Yongjun Kim, Kihun Yu, Jinwoo Han

Electronic Warfare R&D, LIG NEX1 Co., Ltd.

딥 러닝 기법을 이용한 레이더 신호 분류 모델 연구

김용준, 유기훈, 한진우

LIG넥스원(주) 전자전연구소

Abstract

Classification of radar signals in the field of electronic warfare is a problem of discriminating threat types by analyzing enemy threat radar signals such as aircraft, radar, and missile received through electronic warfare equipment. Recent radar systems have adopted a variety of modulation schemes that are different from those used in conventional systems, and are often difficult to analyze using existing algorithms. Also, it is necessary to design a robust algorithm for the signal received in the real environment due to the environmental influence and the measurement error due to the characteristics of the hardware. In this paper, we propose a radar signal classification method which are not affected by radar signal modulation methods and noise generation by using deep learning techniques.

Key Words: Deep Learning, Convolutional Neural Network, Recurrent Neural Network, Radar Signal Classification, Electronic Warfare