A Radar Signal Recognition Approach via IIF-Net Deep Learning Models
Joint Authors
Li, Ji
Wang, Wei
Ou, Jianping
Zhang, Huiqiang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-28
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures.
In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced.
The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD).
According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed.
Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed.
The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments.
The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB.
Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.
American Psychological Association (APA)
Li, Ji& Zhang, Huiqiang& Ou, Jianping& Wang, Wei. 2020. A Radar Signal Recognition Approach via IIF-Net Deep Learning Models. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1138910
Modern Language Association (MLA)
Li, Ji…[et al.]. A Radar Signal Recognition Approach via IIF-Net Deep Learning Models. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1138910
American Medical Association (AMA)
Li, Ji& Zhang, Huiqiang& Ou, Jianping& Wang, Wei. A Radar Signal Recognition Approach via IIF-Net Deep Learning Models. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1138910
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138910