Open Access
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
Article Number 02012
Number of page(s) 7
Section Communication Technology Security Network
Published online 19 May 2022
  1. Iglesias V, Grajal J, Royer P, et al. Real-time low-complexity automatic modulation classifier for pulsed radar signals[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 108-126. [CrossRef] [Google Scholar]
  2. Zhang M M, Liu Y A, Song P. Applications of Partial Connection Clustering Algorithm and Random Forest Algorithm in Radar Signal Sorting[J]. Laser & Optoelectronics Progress, 2019, 56(06): 236-243. [Google Scholar]
  3. Chen K, Zhang S, Zhu L, et al. Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning[J]. Sensors, 2021, 21(2): 449. [Google Scholar]
  4. Qu Z, Mao X, Deng Z. Radar Signal Intra-pulse Modulation Recognition Based on Convolutional Neural Network[J]. IEEE Access, 2018, 6: 43874-43884. [CrossRef] [Google Scholar]
  5. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 26-July 1, 2016, Las Vegas, Nevada, USA, IEEE, 2016: 770-778. [Google Scholar]
  6. Wang Q, Du P, Yang J, et al. Transferred deep learning based waveform recognition for cognitive passive radar[J]. Signal processing, 2019, 155(FEB.): 259-267. [CrossRef] [Google Scholar]
  7. Wu B, Yuan S, Li P, et al. Radar Emitter Signal Recognition Based on OneDimensional Convolutional Neural Network with Attention Mechanism[J]. Sensors, 2020, 20(21):6350. [Google Scholar]
  8. Shi L M, Yang C Z, Wu H C. Radar signal recognition method based on deep residual network and triplet loss[J]. Systems Engineering and Electronics, 2020, 42(11): 25062512. [Google Scholar]
  9. Xu Z J, Yang W T, Yang C Z, et al. Improved residual neural network algorithm for radar intra-pulse modulation classification[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(4): 7. [Google Scholar]
  10. Qin X, Huang J, Cha X, et al. Radar Emitter Signal Recognition Based on Dilated Residual Network [J]. ACTA ELECTRONICA SINICA, 2020, 48(3): 7. [Google Scholar]
  11. Zhao M, Zhong S, Fu X, et al. Deep Residual Shrinkage Networks for Fault Diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, PP(99):1-1. [Google Scholar]
  12. Jie H, Li S, Gang S. Squeeze-and-Excitation Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 19-21, 2018, Salt Lake City, State of Utah, USA, IEEE, 2018: 7132-7141. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.