基于递进神经网络的混叠干扰识别技术

doi:10.12305/j.issn.1001-506X.2025.10.12

摘要/Abstract

摘要：

针对战场实战电磁对抗中，多干扰机协同作战会导致多种不同雷达干扰信号同时存在，使用传统卷积神经网络对大量混叠干扰进行识别存在规模大、难以精细化识别干扰类型的问题，提出一种递进式卷积神经网络，通过分步算法分别提取存在的混叠类型特征以及干扰类型特征。通过对多种混叠干扰信号时频分析，构建训练集与测试集对网络进行训练。仿真实验表明，该网络对同时存在的3种混叠类型下的15种不同干扰信号，可以达到99.3889%以上的识别准确率，在不同干噪比条件下识别效能明显优于传统卷积神经网络。

关键词: 递进神经网络, 混叠干扰信号, 特征提取, 干扰识别

Abstract:

In practical electromagnetic warfare scenarios, where multiple jammers operating in coordination create a complex environment with various radar jamming signals existing simultaneously, traditional convolutional neural networks （CNNs） struggle with the scale and specificity required for effective interference classification. This paper introduces a stepwise CNN, which addresses these challenges by employing a staged approach to separately extract overlap type feature and interference type features. Through time-frequency analysis of various overlapping jamming signals, training and fest datasets are developed to train the network. Simulation experiments show that the proposed network achieves a recognition accuracy rate of over 99.3889% for fifteen different jamming signals under three types of mixed interference conditions. The recognition performance under different signal-to-noise ratio conditions is significantly better than traditional CNNs.

Key words: stepwise neural network, overlap interference, feature extraction, interference recognition

中图分类号:

TN 972

付亦凡, 阮航, 穆贺强, 周东平, 潘黎, 雷蕾, 鲍嘉瑞. 基于递进神经网络的混叠干扰识别技术[J]. 系统工程与电子技术, 2025, 47(10): 3251-3256.

Yifan FU, Hang RUAN, Heqiang MU, Dongping ZHOU, Li PAN, Lei LEI, Jiarui BAO. Overlapping interference recognition based on stepwise neural network[J]. Systems Engineering and Electronics, 2025, 47(10): 3251-3256.

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参考文献 30

1	赵国庆. 雷达对抗原理[M]. 西安: 西安电子科技大学出版社, 2015.
	ZHAO G Q. Principles of radar countermeasures[M]. Xi’an: Xidian University Press.
2	刘春生, 郝治理, 王伟. 人工智能技术在雷达对抗中的应用[J]. 科技导报, 2019, 37 (4): 63- 68.
	LIU C S, HAO Z L, WANG W. Application of artificial intelligence technology in radar countermeasures[J]. Science & Technology Review, 2019, 37 (4): 63- 68.
3	LEI A, FAN W, ZHOU F. A cognitive radar anti-jamming strategy generation algorithm based on dueling double DQN[C]//2023 IEEE International Radar Conference, 2023: 1−5.
4	BANG J H, PARK D H, KIM H N. Improved TOA and pulse width estimation for wideband signal in electronic warfare systems[C]//Proc. of the 19th European Radar Conference, 2022: 73−76.
5	ATA H A, FURKAN U, ORKUN A. Calibration of sensitivity and noise floor levels on electronic warfare systems[C]//Proc. of the IEEE AUTOTESTCON, 2023.
6	CANISIO B, EDGARD C. SoC-based multichannel STFT generator for digital electronic warfare receivers[C]//Proc. of the IEEE 42nd International Conference of the Chilean Computer Science Society, 2023.
7	LUO Z B, XING M D. Target imaging and anti-jamming with frequency agile OAM radar[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21, 3506705.
8	HUANG L J, SONG C, JIN G D, et al. Multiscale barrage jamming against SAR: a jamming signal modulation method and a template design method[J]. IEEE Trans. on Aerospace and Electronic System, 2023, 60 (4): 4543- 4558.
9	YU L, LI J Q, WEI Y S. A novel jamming signal recognition method based on data augmentation using 1D-GAN under small sample condition[C]//Proc. of the IEEE International Radar Conference, 2023.
10	LI X Y, DONG S B, YU M Y. Incremental radar jamming recognition method based on enhanced attention and dynamic weights[C]//Proc. of the 5th International Academic Exchange Conference on Science and Technology Innovation, 2023: 470−476.
11	张海舟, 贺青, 马泽强, 等. 基于轻量化卷积神经网络的雷达干扰识别技术研究[J]. 现代雷达, 2024, 46 (6): 79- 84.
	ZHANG H Z, HE Q, MA Z Q, et al. Research on radar jamming recognition technology based on lightweight convolutional neural network[J]. Modern Radar, 2024, 46 (6): 79- 84.
12	郭立民, 鄂璟仪, 黄文青. 基于改进MobileV3Net的脉冲雷达干扰识别方法[J]. 舰船电子对抗, 2024, 47 (4): 1- 7.
	GUO L M, E J Y, HUANG W Q. Method recogniting the jamming to pulse radars based on improved MobileV3Net[J]. Shipboard Electronic Countermeasure, 2024, 47 (4): 1- 7.
13	张浩. 基于深度学习的成像雷达干扰抑制方法研究[D]. 成都: 电子科技大学, 2024.
	ZHANG H. Research on deep learning-based imaging radar interference suppression method[D]. Chengdu: University of Electronic Science and Technology of China, 2024.
14	ZHOU H, TIAN M H, GUO J M. System architecture of intelligent radar[C]//Proc. of the CIE International Conference on Radar, 2021: 3210−3214.
15	ZHU Y C, ZHOU Q B, SUN Z H. Study on the calculation of the best rotation period of electronic equipment war storage materials based on economic life[C]//Proc. of the IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence, 2021: 700−704.
16	RAHUL H, UPASNA S, MANISH K. Cyber and electronic warfare in context of defence forces in present scenario[C]//Proc. of the 2nd International Conference on Electrical, Electronics, Information and Communication Technologies, 2023.
17	ALI A K, SATTAR B S. Intelligence and electronic warfare: challenges and future trends[C]//Proc. of the 7th International Conference on Contemporary Information Technology and Mathematics, 2021: 118−123.
18	YANG B Y, LI K, JIU B, et al. An intelligent jamming strategy design method against frequency agility radar[C]//Proc. of the IEEE International Radar Conference, 2023.
19	杨兴宇, 阮怀林. 基于双谱分析的雷达有源欺骗干扰识别[J]. 探测与控制学报, 2018, 40 (2): 122- 127.
	YANG X Y, RUAN H L. Active deception jamming identification method based on bispectrum analysis[J]. Journal of Detection & Control, 2018, 40 (2): 122- 127.
20	杨少奇, 田波, 周瑞钊. 应用双谱分析和分形维数的雷达欺骗干扰识别[J]. 西安交通大学学报, 2016, 50 (12): 128- 135.
	YANG S Q, TIAN B, ZHOU R Z. A jamming identification method against radar deception based on bispectrum analysis and fractal dimension[J]. Journal of Xi’an Jiaotong University, 2016, 50 (12): 128- 135.
21	XU C, YU L, WEI Y S, et al. Research on active jamming recognition in complex electromagnetic environment[C]//Proc. of the IEEE International Conference on Signal, Information and Data Processing, 2019.
22	唐陈, 王峰. 基于卷积神经网络的雷达干扰识别技术研究[J]. 中国电子科学研究院学报, 2022, 17 (1): 63- 70.
	TANG C, WANG F. Research on radar jamming recognition technology based on convolution neural network[J]. Journal of China Academy of Electronics and Information Technology, 2022, 17 (1): 63- 70.
23	ZHAO Q Y, LIU Y. Research on electronic jamming identification based on CNN[C]//Proc. of the IEEE International Conference on Signal, Information and Data Processing, 2019.
24	QIAN J X, TENG X Y, QIU Z Y. Recognition of radar deception jamming based on convolutional neural network[C]//Proc. of the IET International Radar Conference, 2020: 913−918.
25	刘强. 基于深度学习的雷达干扰识别技术[D]. 成都: 电子科技大学, 2020.
	LIU Q. Radar interference recognition based on deep learning[D]. Chengdu: University of Electronic Science and Technology of China, 2024.
26	LI H, FANG X L, ZHANG L, et al. Semi-supervised open-set recognition of radar active jamming[C]//Proc. of the CIE International Conference on Radar, 2021: 2168−2171.
27	宋星, 复杂电磁环境下的雷达干扰智能感知方案研究[D]. 西安: 西安电子科技大学, 2022.
	SONG X. Research on intelligent perception method ofradar jamming in complex electromagneticenvironment[D]. Xi’an: Xidian University, 2022.
28	LIN A N, MA Z Y, HUANG Z, et al. Unknown radar waveform recognition based on transferred deep learning[J]. IEEE Access, 2020, 8, 793- 807.
29	LYU Q Z, QUAN Y H, FENG W, et al. Radar deception jamming recognition based on weighted ensemble CNN with transfer learning[J]. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5107511.
30	MOLCHANOV P, TYREE S, KARRAS T, et al. Pruning convolutional neural networks for resource efficient inference[EB/OL]. [2024-11-22]. https://arxiv.org/abs/1611.06440.

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