Radio LFM Fuze Interference Identification Based on Convolutional Neural Network and Attention Mechanism-Reference-Cited by-同舟云学术

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Radio LFM Fuze Interference Identification Based on Convolutional Neural Network and Attention Mechanism

Published:2023-10-20 Issue: Volume:41 Page:1410-1414
ISSN:
Container-title:2023 IEEE 23rd International Conference on Communication Technology (ICCT)
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Author:

Xian Zhaoxia¹,Bai Zhiquan¹,Yang Jikai¹,Zhao Jinqiu¹,Wang Caifeng²,Hao Xinhong³

Affiliation:

1. Shandong Provincial Key Lab. of Wireless Communication Technologies,School of Information Science and Engineering, Shandong University,Qingdao,Shandong,China

2. School of Computer and Information Engineering, Qilu Institute of Technology,Jinan,Shandong,China

3. School of Mechatronical Engineering, Beijing Institute of Technology,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10419380/10419200/10419606.pdf?arnumber=10419606

Reference13 articles.

1. The dificiencies of domestic fuze safety refering to U.S. millitary standards;Wang;Journal of Detection & Control,2019

2. Anti-jamming Method of Radio Fuze Based on KNN

3. Discrimination between radar targets and deception jamming in distributed multiple‐radar architectures

4. Recognition of Jamming and Target Sig-nal for Pulse Doppler Fuze Based on FCM Algorithm with Incremental Update;Dai;Acta Armamentarii,2018

5. The interference classification and recognition based on SF-SVM algorithm

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