| Prediction of Jamming Techniques by Using LSTM |
|
Gyeong-Hoon Lee, Jeil Jo, Cheong Hee Park |
1Department of Computer Science and Engineering, Chungnam National University
2The 2nd Research and Development Institute, Agency for Defense Development |
| LSTM을 이용한 재밍 기법 예측 |
|
이경훈, 조제일, 박정희 |
1충남대학교 컴퓨터공학과
2국방과학연구소 제2기술연구본부 |
|
|
| Abstract |
|
Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals. |
| Key Words:
Jamming, Radar Signal, Deep Learning, LSTM |
|
PDF Links
Full text via DOI
Download Citation
