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http://dx.doi.org/10.9766/KIMST.2019.22.2.170

Research for Radar Signal Classification Model Using Deep Learning Technique  

Kim, Yongjun (Electronic Warfare R&D, LIG NEX1 Co., Ltd.)
Yu, Kihun (Electronic Warfare R&D, LIG NEX1 Co., Ltd.)
Han, Jinwoo (Electronic Warfare R&D, LIG NEX1 Co., Ltd.)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.22, no.2, 2019 , pp. 170-178 More about this Journal
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.
Keywords
Deep Learning; Convolutional Neural Network; Recurrent Neural Network; Radar Signal Classification; Electronic Warfare;
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