Radar Deception Jamming Recognition Method Based on Domain Adaptation and Attention Mechanism

SUN Minhong; CHEN Xinwei; QIU Zhaoyang; TENG Xuyang

doi:10.11999/JEIT210871

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3891-3899

SUN Minhong, CHEN Xinwei, QIU Zhaoyang, TENG Xuyang. Radar Deception Jamming Recognition Method Based on Domain Adaptation and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3891-3899. doi: 10.11999/JEIT210871

Citation:

SUN Minhong, CHEN Xinwei, QIU Zhaoyang, TENG Xuyang. Radar Deception Jamming Recognition Method Based on Domain Adaptation and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3891-3899. doi: 10.11999/JEIT210871

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Radar Deception Jamming Recognition Method Based on Domain Adaptation and Attention Mechanism

doi: 10.11999/JEIT210871

College of Communication Engineerin, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The Characteristic Discipline Program of National Defense (JCKY2019415D002)

Received Date: 2021-08-24
Accepted Date: 2022-03-03
Rev Recd Date: 2022-02-21

Available Online: 2022-03-09

Publish Date: 2022-11-14

Abstract

Abstract

Considering solving the problem that the application of conventional feature recognition methods is limited and the depth learning method needs a large amount of labeled data to achieve high recognition performance in radar deception jamming recognition, a domain adaptive radar deception jamming recognition method based on depth residual model is proposed to improve the labeling limit. The attention mechanism is integrated to improve further the recognition accuracy. Firstly, after the time-frequency transformation of the radar received signal, the domain adaptation technology based on the idea of countermeasure network is applied to realize the migration recognition from labeled source domain samples to unlabeled target domain samples. Secondly, through the designed spatial channel attention residual module, the network training focuses on the global spatial features and high response channels of the time-frequency image, so as to ignore the areas with low mobility in the time-frequency image and suppress the generation of negative migration. Experimental results on radar deception jamming data sets in different source and target domains show the feasibility and effectiveness of the proposed method.
- Radar anti-jamming,
- Deception jamming recognition,
- Transfer learning,
- Domain adaptation,
- Attention mechanism

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References(21)

References

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