面向小样本的多模态雷达有源欺骗干扰识别方法

张顺生; 陈爽; 陈晓莹; 刘莹; 王文钦

doi:10.12000/JR23104

面向小样本的多模态雷达有源欺骗干扰识别方法

DOI: 10.12000/JR23104

张顺生^1, ,,
陈爽^1,,
陈晓莹²,
刘莹²,
王文钦³

1.
电子科技大学电子科学技术研究院成都 611731
2.
中国科学院大学计算机科学与技术学院北京 100190
3.
电子科技大学信息与通信工程学院成都 611731

基金项目: 国家部委基金

详细信息

作者简介:
张顺生，博士，研究员，主要研究方向为新体制雷达探测与成像、人工智能技术在雷达、电子战中的应用等

陈　爽，硕士生，主要研究方向为雷达有源欺骗干扰识别

陈晓莹，博士生，主要研究方向为雷达信号处理、雷达抗干扰和认知雷达

刘　莹，博士，教授，主要研究方向为数据挖掘、人工智能和高性能计算等

王文钦，博士，教授，主要研究方向为新体制雷达、雷达信号处理和电子对抗技术

通讯作者:
张顺生 zhangss@uestc.edu.cn

责任主编：毕大平 Corresponding Editor: BI Daping
中图分类号: TN958
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- 被引次数: 0
出版历程
- 收稿日期: 2023-06-11
- 修回日期: 2023-07-22
- 网络出版日期: 2023-08-15
- 刊出日期: 2023-08-28

Active Deception Jamming Recognition Method in Multimodal Radar Based on Small Samples

1.
Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China
3.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Ministries Foundation

More Information

Corresponding author: ZHANG Shunsheng, zhangss@uestc.edu.cn

摘要

摘要: 干扰识别是雷达抗干扰的前提，但对于实际的雷达欺骗干扰识别，存在着样本数量不足的问题。针对此问题，该文提出一种面向小样本的多模态雷达有源欺骗干扰识别方法。该方法基于雷达信号提取的特征参数及时频图像两种模态信息，利用原型网络训练多模态特征，并借助图像降噪处理和加权欧氏距离提高低信噪比下的识别性能，实现小样本条件下的雷达欺骗干扰识别。仿真结果表明，该文所提方法在干信比为3 dB时，10种雷达欺骗干扰的平均识别准确率达到了97%以上。模拟器数据的测试结果表明所提方法具备良好的泛化能力。
- 雷达有源欺骗干扰 /
- 多模态 /
- 小样本 /
- 原型网络 /
- 干扰识别
Abstract: Jamming recognition is a prerequisite for radar antijamming and actual radar deception jamming recognition; however, there is a problem of insufficient samples. To address this issue, we propose a multimodal radar active deception jamming recognition method based on small samples in this paper. This method is based on two modal information—feature parameters and time-frequency images extracted from radar signals—and utilizes prototype networks to train multimodal features. Furthermore, the model adopts the image denoising method and weighted Euclidean distance to improve the recognition performance at low signal-to-noise ratios. Thus, radar deception jamming recognition can be achieved under small sample conditions. Simulation results reveal that the proposed method achieves an average recognition accuracy of over 97% across 10 types of radar deception jamming when the jamming-to-signal ratio is 3 dB. Moreover, the test results from the simulator data verify the good generalization performance of the proposed method.
- Radar active deception jamming /
- Multimodal /
- Small samples /
- Prototypical networks /
- Jamming recognition

HTML全文

图 1 预处理前的时频图像

Figure 1. Time-frequency images before preprocessing

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图 2 预处理后的时频图像

Figure 2. Time-frequency images after preprocessing

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图 3 原型网络基本架构图

Figure 3. Basic architecture of prototype network

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图 4 多模态特征融合网络

Figure 4. Multimodal feature fusion network

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图 5 本文方法的混淆矩阵(平均识别率97.65%)

Figure 5. Confusion matrix of the proposed method (average recognition rate 97.65%)

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图 6 不同网络在不同信噪比下识别性能比较

Figure 6. Comparison of recognition performance of different networks under different signal-to-noise ratios

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图 7 干扰信号数据获取平台

Figure 7. Interference signal data acquisition platform

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图 8 泛化能力测试结果

Figure 8. Generalization ability test results

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表 1 不同网络的复杂度参数表

Table 1. Table of complexity parameters for different networks

网络	FLOPs (G)	Params (M)
网络${\phi _{\rm{T}}}$	0.000339968	0.041760
网络${\phi _{\rm{P}}}$	0.165375488	0.186048
时频图像网络^[23]	0.216158208	0.186048
特征参数网络^[24]	0.000408576	0.041760
孪生网络^[25]	3.395630784	138.357544

下载: 导出CSV

表 2 回波信号与欺骗干扰信号基本参数设置

Table 2. Basic parameter setting of echo signal and jamming signal

信号类型	参数	参数值
回波与干扰信号	载波	5.5 GHz
	带宽	10 MHz
	采样率	40 MHz
	脉冲宽度	50 μs
	脉冲重复频率	4000 Hz
	信噪比	–10～10 dB
	干信比	3 dB
密集假目标干扰	假目标数量	3～7
切片重构干扰	矩形脉冲串的个数	3～5
切片重构干扰	每一段填充的时隙数	3～5
间歇采样转发干扰	间歇采样脉冲宽度	7～9 μs
间歇采样转发干扰	间歇采样周期	12～15 μs
频谱弥散干扰	采样倍数	3～5
卷积灵巧噪声干扰	噪声带宽倍数	0.3～0.7
距离拖引干扰	拖引速度	300～600 m/s
速度拖引干扰	拖引加速度	50～200 m/s²
距离速度联合拖引干扰	拖引速度	300～600 m/s
距离速度联合拖引干扰	拖引加速度	50～200 m/s²

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表 3 不同训练样本数下识别性能比较(%)

Table 3. Comparison of recognition performance under different numbers of training samples (%)

训练信噪比	2个训练样本	5个训练样本	10个训练样本	15个训练样本	20个训练样本
–10 dB	75.02	92.12	93.46	92.23	92.42
–5 dB	89.14	91.10	91.29	93.24	93.12
0 dB	86.11	92.18	92.29	94.89	93.27
5 dB	84.74	92.83	94.79	89.05	94.97
10 dB	83.17	87.73	87.85	88.41	90.67
随机信噪比	85.18	93.95	96.25	97.56	97.64

下载: 导出CSV

参考文献(26)

[1]	KWAK C M. Application of DRFM in ECM for pulse type radar[C]. 2009 34th International Conference on Infrared, Millimeter, and Terahertz Waves, Busan, Korea (South), 2009: 1–2.
[2]	ZHANG Haoyu, YU Lei, CHEN Yushi, et al. Fast complex-valued CNN for radar jamming signal recognition[J]. Remote Sensing, 2021, 13(15): 2867. doi: 10.3390/rs13152867
[3]	陈思伟, 崔兴超, 李铭典, 等. 基于深度CNN模型的SAR图像有源干扰类型识别方法[J]. 雷达学报, 2022, 11(5): 897–908. doi: 10.12000/JR22143 CHEN Siwei, CUI Xingchao, LI Mingdian, et al. SAR image active jamming type recognition based on deep CNN model[J]. Journal of Radars, 2022, 11(5): 897–908. doi: 10.12000/JR22143
[4]	MENDOZA A, SOTO A, and FLORES B C. Classification of radar jammer FM signals using a neural network[C]. SPIE 10188, Radar Sensor Technology XXI, Anaheim, USA, 2017: 101881G.
[5]	WU Zhilu, ZHAO Yanlong, YIN Zhengdong, et al. Jamming signals classification using convolutional neural network[C]. 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain, 2017: 62–67.
[6]	ZHAO Qingyuan, LIU Yang, CAI Linjie, et al. Research on electronic jamming identification based on CNN[C]. 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China, 2019: 1–5.
[7]	LIU Qiang and ZHANG Wei. Deep learning and recognition of radar jamming based on CNN[C]. 2019 12th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 2019: 208–212.
[8]	HOWARD J and RUDER S. Universal language model fine-tuning for text classification[C]. The 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018: 328–339.
[9]	DING Kaize, WANG Jianling, LI Jundong, et al. Graph prototypical networks for few-shot learning on attributed networks[C]. The 29th ACM International Conference on Information & Knowledge Management, Online, 2020: 295–304.
[10]	CHOPRA S, HADSELL R, and LECUN Y. Learning a similarity metric discriminatively, with application to face verification[C]. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, USA, 2005: 539–546.
[11]	SHAO Guangqing, CHEN Yushi, and WEI Yinsheng. Convolutional neural network-based radar jamming signal classification with sufficient and limited samples[J]. IEEE Access, 2020, 8: 80588–80598. doi: 10.1109/ACCESS.2020.2990629
[12]	陈泽伟, 严远鹏. 基于改进DCGAN的毫米波雷达相互干扰时频图像生成研究——以生成样本对CNN干扰抑制模型性能影响为例[J]. 现代信息科技, 2022, 6(13): 55–61. doi: 10.19850/j.cnki.2096-4706.2022.013.014 CHEN Zewei and YAN Yuanpeng. Research on generation of MMW radar mutual interference time-frequency image based on improved DCGAN—a case of performance effect of the generated samples on the CNN interference suppression model[J]. Modern Information Technology, 2022, 6(13): 55–61. doi: 10.19850/j.cnki.2096-4706.2022.013.014
[13]	KOCH G, ZEMEL R, and SALAKHUTDINOV R. Siamese neural networks for one-shot image recognition[C]. The 32nd International Conference on Machine Learning, Lille, France, 2015: 1–8.
[14]	SNELL J, SWERSKY K, and ZEMEL R S. Prototypical networks for few-shot learning[C]. 31st Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 4080–4090.
[15]	LU Yunlong and LI Siyu. CFAR detection of DRFM deception jamming based on singular spectrum analysis[C]. 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xiamen, China, 2017: 1–6.
[16]	定少浒, 汤建龙. 基于SSA的DRFM速度欺骗干扰识别[J]. 雷达科学与技术, 2020, 18(1): 44–50. doi: 10.3969/j.issn.1672-2337.2020.01.008 DING Shaohu and TANG Jianlong. DRFM velocity deception jamming recognition based on singular spectrum analysis[J]. Radar Science and Technology, 2020, 18(1): 44–50. doi: 10.3969/j.issn.1672-2337.2020.01.008
[17]	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, 2021, 60: 5107511. doi: 10.1109/TGRS.2021.3129645
[18]	JAMIL N, SEMBOK T M T, and BAKAR Z. A. Noise removal and enhancement of binary images using morphological operations[C]. 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2008: 1–6.
[19]	XI Bobo, LI Jiaojiao, LI Yunsong, et al. Deep prototypical networks with hybrid residual attention for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 3683–3700. doi: 10.1109/JSTARS.2020.3004973
[20]	利强, 张伟, 金秋园, 等. 基于知识原型网络的小样本多功能雷达工作模式识别[J]. 电子学报, 2022, 50(6): 1344–1350. doi: 10.12263/DZXB.20210932 LI Qiang, ZHANG Wei, JIN Qiuyuan, et al. Multi-function radar working mode recognition with few samples based on knowledge embedded prototype network[J]. Acta Electronica Sinica, 2022, 50(6): 1344–1350. doi: 10.12263/DZXB.20210932
[21]	GAO Meng, LI Hongtao, JIAO Bixuan, et al. Simulation research on classification and identification of typical active jamming against LFM radar[C]. Proceedings of SPIE 11384 Eleventh International Conference on Signal Processing Systems, Nanjing, China, 2019: 113840T.
[22]	LAKE B M, SALAKHUTDINOV R, and TENENBAUM J B. The Omniglot challenge: A 3-year progress report[J]. Current Opinion in Behavioral Sciences, 2019, 29: 97–104. doi: 10.1016/j.cobeha.2019.04.007
[23]	WANG Jingyi, DONG Wenhao, and SONG Zhiyong. Radar active jamming recognition based on time-frequency image classification[C]. 2021 5th International Conference on Electronic Information Technology and Computer Engineering, Xiamen, China, 2021: 449–454.
[24]	TIAN Xinyi, CHEN Baixiao, and ZHANG Zhaoming. Multiresolution jamming recognition with few-shot learning[C]. 2021 CIE International Conference on Radar, Haikou, China, 2021: 2267–2271.
[25]	梁先明. 一种优化孪生网络的小样本辐射源个体识别方法[J]. 电讯技术, 2022, 62(6): 695–701. doi: 10.3969/j.issn.1001-893x.2022.06.001 LIANG Xianming. An emitter individual identification method for small samples based on optimized siamese networks[J]. Telecommunication Engineering, 2022, 62(6): 695–701. doi: 10.3969/j.issn.1001-893x.2022.06.001
[26]	GU Jiuxiang, WANG Zhenhua, KUEN J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77: 354–377. doi: 10.1016/j.patcog.2017.10.013