Compound Active Jamming Recognition for Zero-memory Incremental Learning

WU Zhenhua; CUI Jinxin; CAO Yice; ZHANG Qiang; ZHANG Lei; YANG Lixia

doi:10.11999/JEIT240521

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WU Zhenhua, CUI Jinxin, CAO Yice, ZHANG Qiang, ZHANG Lei, YANG Lixia. Compound Active Jamming Recognition for Zero-memory Incremental Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240521

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WU Zhenhua, CUI Jinxin, CAO Yice, ZHANG Qiang, ZHANG Lei, YANG Lixia. Compound Active Jamming Recognition for Zero-memory Incremental Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240521

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Compound Active Jamming Recognition for Zero-memory Incremental Learning

doi: 10.11999/JEIT240521

WU Zhenhua^{1, 2},
CUI Jinxin¹,
CAO Yice^{1
,
,},
ZHANG Qiang³,
ZHANG Lei⁴,
YANG Lixia¹

1.
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China
2.
Thirty-eighth Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China
3.
National Key Laboratory of Space Integrated Information System, Beijing 100094, China
4.
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds: The National Natural Science Foundation of China (62201007, 62401007), China Postdoctoral Science Foundation (2020M681992), The Natural Science Foundation of Anhui (2308085QF199)

Received Date: 2024-06-25
Rev Recd Date: 2024-11-07

Available Online: 2024-11-13

Abstract

Abstract

Under the non-complete and highly dynamic active jamming confrontation combat environment, the static model optimized and trained for multiple types of single active jamming samples in the library at this stage is unable to update the model quickly and difficult to cope with the problem of imbalance of test samples in the face of compound jamming outside the library of diverse types, variable parameters and multiple combinations. Considering this problem, a zero-memory incremental learning-based radar compound active jamming recognition method is proposed in this paper. Firstly, a meta-learning training model is utilized to learn a prototype of a single jamming within the library, and an efficient feature extractor is trained so that it has the ability to effectively extract the features of the compound jamming outside the library. Further, based on the hyperdimensional space and cosine similarity calculation, a Zero-Memory Incremental Learning Network (ZMILN) is constructed to map the compound jamming prototype vectors into the hyperdimensional space and store them, so as to realize the dynamic update of the recognition model. In addition, in order to solve the compound jamming recognition problem under sample imbalance, the Transductive Information Maximization (TIM) test module is designed to further strengthen the training of the recognition model to cope with the imbalanced test samples by introducing scatter constraints in the mutual information loss function. Experimental results show that the method proposed in this paper achieves an average recognition accuracy of 93.62% after incremental learning for four types of single jamming and seven types of compound jamming under imbalance test conditions. The method realizes fast and dynamic recognition of compound jamming outside the library under multiple combination conditions by fully extracting the knowledge of multiple types of single jamming in the library.
- Radar active jamming,
- Zero-memory incremental learning,
- Imbalance,
- Transductive Information Maximization(TIM),
- Compound jamming recognition

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References(26)

References

[1]	魏毅寅, 杨文华. 海战场典型干扰对抗场景及反舰导弹应对策略研究[J]. 战术导弹技术, 2020(5): 1–8. doi: 10.16358/j.issn.1009-1300.2020.1.538. WEI Yiyin and YANG Wenhua. Study on typical jamming scenes in naval battle field and countermeasures of anti-ship missile[J]. Tactical Missile Technology, 2020(5): 1–8. doi: 10.16358/j.issn.1009-1300.2020.1.538.
[2]	ZHANG Xiang, LAN Lan, ZHU Shengqi, et al. Intelligent suppression of interferences based on reinforcement learning[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(2): 1400–1415. doi: 10.1109/TAES.2023.3336643.
[3]	崔国龙, 余显祥, 魏文强, 等. 认知智能雷达抗干扰技术综述与展望[J]. 雷达学报, 2022, 11(6): 974–1002. doi: 10.12000/JR22191. CUI Guolong, YU Xianxiang, WEI Wenqiang, et al. An overview of antijamming methods and future works on cognitive intelligent radar[J]. Journal of Radars, 2022, 11(6): 974–1002. doi: 10.12000/JR22191.
[4]	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. doi: 10.16337/j.1004-9037.2022.01.001. ZHOU Hongping, WANG Ziwei, and GUO Zhongyi. Overview on recognition algorithms of radar active jamming[J]. Journal of Data Acquisition and Processing, 2022, 37(1): 1–20. doi: 10.16337/j.1004-9037.2022.01.001.
[5]	QU Qizhe, WEI Shunjun, LIU Shan, et al. JRNet: Jamming recognition networks for radar compound suppression jamming signals[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15035–15045. doi: 10.1109/TVT.2020.3032197.
[6]	ZHANG Jiaxiang, LIANG Zhennan, ZHOU Chao, 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. doi: 10.1109/TAES.2022.3224695.
[7]	LV Qinzhe, QUAN Yinghui, FENG Wei, et al. Radar deception jamming recognition based on weighted ensemble CNN with transfer learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5107511. doi: 10.1109/TGRS.2021.3129645.
[8]	DU Jinbiao, FAN Weiwei, GONG Chen, et al. Aggregated-attention deformable convolutional network for few-shot SAR jamming recognition[J]. Pattern Recognition, 2024, 146: 109990. doi: 10.1016/j.patcog.2023.109990.
[9]	LUO Zhenyu, CAO Yunhe, YEO T S, et al. Few-shot radar jamming recognition network via time-frequency self-attention and global knowledge distillation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5105612. doi: 10.1109/TGRS.2023.3280322.
[10]	ZHOU Hongping, WANG Lei, GUO Zhongyi. Recognition of radar compound jamming based on convolutional neural network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(6): 7380–7394. doi: 10.1109/TAES.2023.3288080.
[11]	LI Boran, ZHANG Lei, DAI Jingwei, et al. FETTrans: Analysis of compound interference identification based on bidirectional dynamic feature adaptation of improved transformer[J]. IEEE Access, 2022, 10: 66321–66331. doi: 10.1109/ACCESS.2022.3182010.
[12]	MENG Yunyun, YU Lei, and WEI Yinsheng. Multi-label radar compound jamming signal recognition using complex-valued CNN with jamming class representation fusion[J]. Remote Sensing, 2023, 15(21): 5180. doi: 10.3390/rs15215180.
[13]	ZHOU Hongping, WANG Lei, MA Minghui, et al. Compound radar jamming recognition based on signal source separation[J]. Signal Processing, 2024, 214: 109246. doi: 10.1016/j.sigpro.2023.109246.
[14]	LV Qinzhe, FAN Hanxin, LIU Junliang, et al. Multilabel deep learning-based lightweight radar compound jamming recognition method[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 2521115. doi: 10.1109/TIM.2024.3400337.
[15]	KONG Yukai, XIA Senlin, DONG Luxin, et al. Compound jamming recognition via contrastive learning for distributed MIMO radars[J]. IEEE Transactions on Vehicular Technology, 2024, 73(6): 7892–7907. doi: 10.1109/TVT.2024.3358996.
[16]	TAO Xiaoyu, HONG Xiaopeng, CHANG Xinyuan, et al. Few-shot class-incremental learning[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 12183–12192. doi: 10.1109/CVPR42600.2020.01220.
[17]	LI Bin, CUI Zongyong, WANG Haohan, et al. SAR incremental automatic target recognition based on mutual information maximization[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 4005305. doi: 10.1109/LGRS. 2024.3368063.
[18]	SERBES A. On the estimation of LFM signal parameters: Analytical formulation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(2): 848–860. doi: 10.1109/TAES.2017.2767978.
[19]	WANG J X. Meta-learning in natural and artificial intelligence[J]. Current Opinion in Behavioral Sciences, 2021, 38: 90–95. doi: 10.1016/j.cobeha.2021.01.002.
[20]	SANTORO A, BARTUNOV S, BOTVINICK M, et al. Meta-learning with memory-augmented neural networks[C]. The 33rd International Conference on International Conference on Machine Learning, New York, USA, 2016: 1842-1850.
[21]	KARUNARATNE G, SCHMUCK M, LE GALLO M, et al. Robust high-dimensional memory-augmented neural networks[J]. Nature Communications, 2021, 12(1): 2468. doi: 10.1038/s41467-021-22364-0.
[22]	VEILLEUX O, BOUDIAF M, PIANTANIDA P, et al. Realistic evaluation of transductive few-shot learning[C]. Proceedings of the 35th International Conference on Neural Information Processing Systems, 2021: 711.
[23]	ZHANG Chi, SONG Nan, LIN Guosheng, et al. Few-shot incremental learning with continually evolved classifiers[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 12455–12464. doi: 10.1109/CVPR46437.2021.01227.
[24]	REBUFFI S A, KOLESNIKOV A, SPERL G, et al. iCaRL: Incremental classifier and representation learning[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2001–2010. doi: 10.1109/cvpr.2017.587.
[25]	SHI Guangyuan, CHEN Jiaxin, ZHANG Wenlong, et al. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima[C]. The 35th International Conference on Neural Information Processing Systems, 2021: 517.
[26]	ZHOU Dawei, WANG Fuyun, YE Hanjia, et al. Forward compatible few-shot class-incremental learning[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 9046–9056. doi: 10.1109/cvpr52688.2022.00884.