Communication Interference Intelligent Recognition in the Air-to-ground Collaboration Scenario

LIU Mingqian; GAO Xiaoteng; LI Ming; ZHU Shouzhong

doi:10.11999/JEIT211260

Volume 44 Issue 3

Mar. 2022

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LIU Mingqian, GAO Xiaoteng, LI Ming, ZHU Shouzhong. Communication Interference Intelligent Recognition in the Air-to-ground Collaboration Scenario[J]. Journal of Electronics & Information Technology, 2022, 44(3): 825-834. doi: 10.11999/JEIT211260

Citation:

LIU Mingqian, GAO Xiaoteng, LI Ming, ZHU Shouzhong. Communication Interference Intelligent Recognition in the Air-to-ground Collaboration Scenario[J]. Journal of Electronics & Information Technology, 2022, 44(3): 825-834. doi: 10.11999/JEIT211260

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Communication Interference Intelligent Recognition in the Air-to-ground Collaboration Scenario

doi: 10.11999/JEIT211260

1.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
2.
The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050050, China
3.
The State 722 Factory, China Electronics Corporation, Guilin 541001, China
4.
Changsha Institute of Passive Location Engineering Technology, Changsha 410008, China

Funds: The National Natural Science Foundation of China (62071364), The Aeronautical Science Foundation of China (2020Z073081001), The Fundamental Research Funds for the Central Universities (JB210104), The 111 Project (B08038)

Received Date: 2021-11-12
Accepted Date: 2022-02-21
Rev Recd Date: 2022-02-19

Available Online: 2022-03-01

Publish Date: 2022-03-28

Abstract

Abstract

In view of the problems that the existing communication interference intelligent recognition methods have low recognition accuracy under the small samples condition and the under-fitting of the network model, an intelligent communication interference recognition method based on twin network in the air-to-ground collaboration scenario is proposed to form the air and ground layout ability. Firstly, the time-frequency diagram, fractional Fourier transform and constellation diagram of the received communication interference signals are extracted as network inputs in the air-to-ground collaboration communication interference cognitive architecture between unmanned air vehicles and ground equipment. Secondly, the network structure based on densely connected convolutional networks is built, and the twin network with dual input weight sharing is designed. Finally, the twin network is trained with random samples, and the benchmark communication interference type feature library is constructed through the twin unilateral network, so as to realize the communication interference intelligent identification. The proposed method evaluates the similarity of samples by measuring the feature distance between two samples, and expands the number of training samples and trains the Siamese network model through the similarity measurement. Simulation results show that the proposed method can effectively realize the communication interference recognition under the support of small data sets, and the recognition performance is significantly improved compared with the existing intelligent recognition methods.
- Communication interference,
- Air-to-ground collaboration,
- Twin network,
- Intelligent recognition,
- Few-shot learning

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References(15)

References

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