Jamming Prediction for Radar Signals Using Machine Learning Methods
Joint Authors
Lee, Gyeong-Hoon
Jo, Jeil
Park, Cheong Hee
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver.
The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types.
However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received.
In this paper, we study two methods of predicting the appropriate jamming technique for a received threat signal using deep learning: using a deep neural network on feature values extracted manually from the PDW list and using long short-term memory (LSTM) which takes the PDW list as input.
Using training data consisting of pairs of threat signals and corresponding jamming techniques, a deep learning model is trained which outputs jamming techniques for threat signal inputs.
Training data are constructed based on the information in the library, but the trained deep learning model is used to predict jamming techniques for received threat signals without using the library.
The prediction performance and time complexity of two proposed methods are compared.
In particular, the ability to predict jamming techniques for unknown types of radar signals which are not used in the stage of training the model is analyzed.
American Psychological Association (APA)
Lee, Gyeong-Hoon& Jo, Jeil& Park, Cheong Hee. 2020. Jamming Prediction for Radar Signals Using Machine Learning Methods. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1208368
Modern Language Association (MLA)
Lee, Gyeong-Hoon…[et al.]. Jamming Prediction for Radar Signals Using Machine Learning Methods. Security and Communication Networks No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1208368
American Medical Association (AMA)
Lee, Gyeong-Hoon& Jo, Jeil& Park, Cheong Hee. Jamming Prediction for Radar Signals Using Machine Learning Methods. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1208368
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208368