零记忆增量学习的复合有源干扰识别

吴振华; 崔金鑫; 曹宜策; 张强; 张磊; 杨利霞

doi:10.11999/JEIT240521

零记忆增量学习的复合有源干扰识别

doi: 10.11999/JEIT240521

吴振华^{1, 2},
崔金鑫¹,
曹宜策^1, ,,
张强³,
张磊⁴,
杨利霞¹

1.
安徽大学信息材料与智能感知安徽省实验室合肥 230601
2.
中国电子科技集团公司第三十八研究所合肥 230088
3.
天基综合信息系统全国重点实验室北京 100094
4.
中山大学电子与通信工程学院深圳 518107

基金项目: 国家自然科学基金(62201007, 62401007)，中国博士后科学基金(2020M681992)，安徽省自然科学基金(2308085QF199)

详细信息

作者简介:
吴振华：男，副教授，研究方向为雷达成像、雷达信号处理、雷达智能干扰对抗

崔金鑫：男，硕士生，研究方向为雷达干扰识别、雷达信号处理

曹宜策：女，讲师，研究方向为遥感图像的智能解译、雷达信号处理、计算机视觉

张强：男，研究员，研究方向为天基信号处理

张磊：男，教授，研究方向为雷达信号处理

杨利霞：男，教授，研究方向为电磁散射与逆散射、电波传播及天线理论与设计、计算电磁学

通讯作者:
曹宜策　yccao@ahu.edu.cn

中图分类号: TN974
计量
- 文章访问数: 111
- HTML全文浏览量: 40
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-25
- 修回日期: 2024-11-07
- 网络出版日期: 2024-11-13

Compound Active Jamming Recognition for Zero-memory Incremental Learning

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)

摘要

摘要: 非完备、高动态有源干扰对抗作战环境下，现阶段针对库内多类型单一有源干扰样本所优化训练的静态模型，在面对库外类型多样、参数多变、组合方式多元的复合干扰时，模型无法快速更新且难以应对测试样本数非均衡问题。针对此问题，该文提出一种基于零记忆增量学习的雷达复合有源干扰识别方法。首先，利用元学习训练模式对库内单一干扰进行原型学习，训练出高效的特征提取器，使其具备对库外复合干扰特征有效提取能力。进而，基于超维空间和余弦相似度计算，构建零记忆增量学习网络(ZMILN)，将复合干扰原型向量映射到超维空间并存储，从而实现识别模型动态更新。此外，为解决样本数非均衡下复合干扰识别问题，设计直推式信息最大化(TIM)测试模块，通过在互信息损失函数中加入散度约束，对识别模型进一步强化训练以应对非均衡测试样本。实验结果表明，该文所提方法在非均衡测试条件下对4种单一干扰和7种复合干扰进行增量学习后，平均识别准确率达到了93.62%。该方法通过对库内多类型单一干扰知识充分提取，实现对多种组合条件下库外复合干扰的快速动态识别。
- 雷达有源干扰 /
- 零记忆增量学习 /
- 非均衡 /
- 直推式信息最大化 /
- 复合干扰识别
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

HTML全文

图 1 雷达回波及干扰信号的时频谱图

下载: 全尺寸图片幻灯片

图 2 零记忆增量学习网络

下载: 全尺寸图片幻灯片

图 3 原型学习网络详细结构图

下载: 全尺寸图片幻灯片

图 4 原型空间更新示意图

下载: 全尺寸图片幻灯片

图 5 不同方法下的干扰识别准确率曲线

下载: 全尺寸图片幻灯片

图 6 1-way 5-shot设置下的t-SNE可视化图

下载: 全尺寸图片幻灯片

图 7 不同基础设置下的干扰识别性能曲线

下载: 全尺寸图片幻灯片

表 1 雷达干扰参数

信号	参数	数值范围
LFM	信号宽度带宽采样频率	10 μs 50 μs 125 MHz
SMSP	干扰个数	3～7
SNJ	转发个数切片长度占空比	3～5 10 μs 0.5～0.8
DFTJ	假目标个数假目标时延	3～7 1～10 μs
MISRJ	转发个数切片长度占空比	3～5 10 μs 0.5～0.8

下载: 导出CSV

表 2 增量干扰数据集配置

阶段	序号	干扰名称	训练样本个数	单次测试样本个数
基础	1	SMSP	100	1
	2	SNJ	100	2
	3	DFTJ	100	2
	4	MISRJ	100	8
增量	5	SNJ+SMSP	5	12
	6	SMSP+MISRJ	5	5
	7	DFTJ+MISRJ	5	9
	8	SMSP+DFTJ	5	5
	9	SNJ+DFTJ	5	12
	10	SNJ+MISRJ	5	7
	11	SNJ+SMSP+DFTJ	5	14

下载: 导出CSV

表 3 TIM模块对模型的影响(%)

测试样本分布	TIM	基础阶段准确率	增量阶段准确率							平均准确率	性能下降率
测试样本分布	TIM	1	2	3	4	5	6	7	8	平均准确率	性能下降率
均衡	√	100.00	100.00	97.95	97.60	96.61	95.72	94.88	94.62	97.17	5.38
均衡	$ \times $	100.00	97.42	92.94	91.70	90.42	89.21	87.61	85.72	91.87	14.28
非均衡	√	100.00	100.00	98.95	97.60	96.61	94.72	93.88	93.62	96.92	6.38
非均衡	$ \times $	99.85	95.22	93.71	87.83	86.44	84.72	72.51	80.66	87.61	19.19

下载: 导出CSV

表 4 在1-way 5-shot设置下不同方法的干扰识别结果

方法	基础阶段准确率(%)	增量阶段准确率(%)							平均准确率 (%)	性能下降率 (%)	训练平均时间 (min)	测试平均时间 (s)
方法	1	2	3	4	5	6	7	8	平均准确率 (%)	性能下降率 (%)	训练平均时间 (min)	测试平均时间 (s)
Ft-CNN^[8]	100.00	92.22	83.33	74.16	67.77	61.33	55.55	51.55	73.23	48.45	50.59	15.73
iCaRL^[24]	100.00	91.14	85.72	85.83	83.51	82.66	80.75	78.47	86.01	21.53	185.15	20.79
TOPIC^[16]	99.31	89.77	83.54	84.91	84.67	81.03	78.75	75.98	84.74	23.33	141.96	19.86
FACT^[26]	97.55	97.77	93.80	92.57	91.29	88.74	85.45	83.12	91.28	14.43	98.15	12.02
CEC^[23]	98.44	96.74	93.94	92.70	90.61	88.72	85.88	84.62	91.45	13.82	169.64	16.95
F2M^[25]	100.00	95.71	93.44	87.70	86.41	84.72	82.45	80.74	88.89	19.26	78.96	9.93
本文(ZMILN)	100.00	100.00	98.95	97.60	96.61	94.72	93.88	93.62	96.92	6.38	62.45	36.95

下载: 导出CSV

参考文献(26)

[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.