Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar
Joint Authors
Wang, Feng
Huang, Shanshan
Liang, Chao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Sensing the external complex electromagnetic environment is an important function for cognitive radar, and the concept of cognition has attracted wide attention in the field of radar since it was proposed.
In this paper, a novel method based on an idea of multidimensional feature map and convolutional neural network (CNN) is proposed to realize the automatic modulation classification of jamming entering the cognitive radar system.
The multidimensional feature map consists of two envelope maps before and after the pulse compression processing and a time-frequency map of the receiving beam signal.
Drawing the one-dimensional envelope in a 2-dimensional plane and quantizing the time-frequency data to a 2-dimensional plane, we treat the combination of the three planes (multidimensional feature map) as one picture.
A CNN-based algorithm with linear kernel sensing the three planes simultaneously is selected to accomplish jamming classification.
The classification of jamming, such as noise frequency modulation jamming, noise amplitude modulation jamming, slice jamming, and dense repeat jamming, is validated by computer simulation.
A performance comparison study on convolutional kernels in different size demonstrates the advantage of selecting the linear kernel.
American Psychological Association (APA)
Wang, Feng& Huang, Shanshan& Liang, Chao. 2020. Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1202022
Modern Language Association (MLA)
Wang, Feng…[et al.]. Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1202022
American Medical Association (AMA)
Wang, Feng& Huang, Shanshan& Liang, Chao. Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1202022
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202022