[1] |
ADAMY D . EW101:电子战基础[M]. 王燕,朱松,译. 北京: 电子工业出版社, 2009.
|
|
ADAMY D . EW101:fundamentals of electronic warfare[M]. Translated by WANG Y,ZHU S. Beijing: Publishing House of Electronics Industry, 2009.
|
[2] |
周红平, 王子伟, 郭忠义 . 雷达有源干扰识别算法综述[J]. 数据采集与处理, 2022,37(1): 1-20.
|
|
ZHOU H P , WANG Z W , GUO Z Y . Overview on recognition algorithms of radar active jamming[J]. Journal of Data Acquisition and Processing, 2022,37(1): 1-20.
|
[3] |
FU R R . Compound jamming signal recognition based on neural networks[C]// Proceedings of the Sixth International Conference on Instrumentation & Measurement,Computer,Communication and Control (IMCCC). Piscataway:IEEE Press, 2016: 737-740.
|
[4] |
ZHU M , LI Y , PAN Z ,et al. Automatic modulation recognition of compound signals using a deep multi-label classifier:a case study with radar jamming signals[J]. Signal Processing, 2019,169(9): 107393.
|
[5] |
ZHANG J X , LIANG Z N , ZHOU C ,et al. Radar compound jamming cognition based on a deep object detection network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023,59(3): 3251-3263.
|
[6] |
李浩 . 基于多标签学习的雷达复合干扰识别研究[D]. 成都:电子科技大学, 2021.
|
|
LI H . Radar compound jamming recognition based on multi-label learning[D]. Chengdu:University of Electronic Science and Technology, 2021.
|
[7] |
QU Q , WEI S , LIU S ,et al. JRNet:jamming recognition networks for radar compound suppression jamming signals[J]. IEEE Transactions on Vehicular Technology, 2020,69(12): 15035-15045.
|
[8] |
庞伊琼, 许华, 张悦 ,等. 基于迁移元学习的调制识别算法[J]. 兵工学报, 2022.doi:10.12382/bgxb.2022.0583.
|
|
PANG Y Q , XU H , ZHANG Y ,et al. Modulation recognition algorithm based on transfer meta-learning[J]. Acta Armamentarii, 2022.doi:10.12382/bgxb.2022.0583.
|
[9] |
季鼎承, 蒋亦樟, 王士同 . 基于域与样例平衡的多源迁移学习方法[J]. 电子学报, 2019,47(3): 692-699.
|
|
JI D C , JIANG Y Z , WANG S T . Multi-source transfer learning method by balancing both the do?mains and instances[J]. Acta Electronica Sinica, 2019,47(3): 692-699.
|
[10] |
SHAO G,CHENY , WEI Y . Convolutional neural network-based radar jamming signal classification with sufficient and limited samples[J]. IEEE Access, 2020(8): 80588-80598.
|
[11] |
LYU Q Z , QUAN Y H , FENG W ,et al. Radar Deception jamming recognition based on weighted ensemble CNN with transfer learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022(60): 1-11.
|
[12] |
CHUNG H , LEE J . Iterative semi-supervised learning using softmax probability[J]. Materials & Continua, 2022,72(3): 5607-5628.
|
[13] |
BAE J , LEE M , KIM S B . Safe semi-supervised learning using a bayesian neural network[J]. Information Sciences, 2022,6(12): 453-464.
|
[14] |
PASSALIS N , IOSIFIDIS A , GABBOUJ M ,et al. Hypersphere-based weight imprinting for few-shot learning on embedded devices[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021,32(2): 925-930.
|
[15] |
ZHUANG F , QI Z , DUAN K ,et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2020,109(1): 43-76.
|
[16] |
BERTHELOT D , CARLINI N , GOODFELLOW I ,et al. MixMatch:a holistic approach to semi-supervised learning[J]. Advances in Neural Information Processing Systems, 2019,32(454): 5050-5060.
|
[17] |
LI P , ZHAO G , XU X . Coarse-to-fine few-shot classification with deep metric learning[J]. Information Sciences, 2022(610): 592-604.
|
[18] |
QIAO S Y , LIU C X , SHEN W ,et al. Few-shot image recognition by predicting parameters from activations[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Piscataway:IEEE Press, 2018: 7229-7238.
|
[19] |
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(CVPR). Piscataway:IEEE Press, 2016: 770-778.
|
[20] |
HAFIZ A M , BHAT R A , HASSABALLAH M . Image classification using convolutional neural network tree ensembles[J]. Multimedia Tools and Applications, 2023,82(5): 6867-6884.
|
[21] |
LI T W , LEE G C . Performance analysis of fine-tune transferred deep learning[C]// Proceedings of the 2021 3rd Eurasia Conference on Communication and Engineering. Piscataway:IEEE Press, 2021: 315-319.
|
[22] |
HIGUCHI Y , MORITZ N , ROUX J L ,et al. Momentum pseudo-labeling:semi-supervised ASR with continuously improving pseudo-labels[J]. IEEE Journal of Selected Topics in Signal Processing, 2022,16(6): 1424-1438.
|
[23] |
CHEN W Y , LIU Y C , KIRA Z ,et al. A closer look at few-shot classification[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019:1904.04232.
|