Distorted Radar Electromagnetic Signal Recognition Based on Meta-learning

YAN Kang; JIN Weidong; HUANG Yingkun; GE Peng; ZHU Jiehao

doi:10.11999/JEIT210190

Volume 44 Issue 4

Apr. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(4): 1351-1357

YAN Kang, JIN Weidong, HUANG Yingkun, GE Peng, ZHU Jiehao. Distorted Radar Electromagnetic Signal Recognition Based on Meta-learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1351-1357. doi: 10.11999/JEIT210190

Citation:

YAN Kang, JIN Weidong, HUANG Yingkun, GE Peng, ZHU Jiehao. Distorted Radar Electromagnetic Signal Recognition Based on Meta-learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1351-1357. doi: 10.11999/JEIT210190

Citation:

PDF( 1254 KB)

Distorted Radar Electromagnetic Signal Recognition Based on Meta-learning

doi: 10.11999/JEIT210190

1.
College of Electrical Engineering , Southwest Jiaotong University, Chengdu 610097, China
2.
The Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China

Funds: The Science and Technology on Electronic Information Control Laboratory (6142105190312)

Received Date: 2021-03-05
Accepted Date: 2021-11-03
Rev Recd Date: 2021-11-03

Available Online: 2021-12-19

Publish Date: 2022-04-18

Abstract

Abstract

Distorted radar electromagnetic signals will seriously affect the detection performance of radar reconnaissance equipment. How to identify effectively the type of distorted signal has important practical significance for the accurate perception of radar systems. For distorted radar signals, there is often a problem of sample scarcity. A Residual Network based on Model-Agnostic Meta-Learning (MAML-ResNet) is proposed. The algorithm first uses normal radar signal samples to train the meta-learner, then the meta-learner is fine-tuned in the distorted signal samples. Finally, the distorted signal is recognized with only a small number of distorted signal samples. Experimental results show that the recognition accuracy of distorted signals under small sample data is effectively improved.
- Distorted radar signal recognition,
- Deep learning,
- Few-shot learning,
- Meta learning

FullText(HTML)

References(22)

References

[1]	袁兴鹏, 张金全, 杨立永. 海面雷达信号环境多路径效应建模与仿真技术研究[J]. 舰船电子对抗, 2013, 36(6): 68–72. doi: 10.16426/j.cnki.jcdzdk.2013.06.008 YUAN Xingpeng, ZHANG Jinquan, and Yang Liyong. Research into modeling and simulation technologies of multi-path effect of sea surface radar signal environment[J]. Shipboard Electronic Countermeasure, 2013, 36(6): 68–72. doi: 10.16426/j.cnki.jcdzdk.2013.06.008
[2]	焦培南, 张忠治. 雷达环境与电波传播特性[M]. 北京: 电子工业出版社, 2007: 101–105. JIAO Peinan and ZHANG Zhongzhi. Radar Environment and Radio Wave Propagation Characteristics[M]. Beijing: Publishing House of Electronics Industry, 2007: 101–105.
[3]	余志斌, 金炜东, 陈春霞. 基于小波脊频级联特征的雷达辐射源信号识别[J]. 西南交通大学学报, 2010, 45(2): 290–295. doi: 10.3969/j.issn.0258-2724.2010.02.022 YU Zhibin, JIN Weidong, and CHEN Chunxia. Radar emitter signal recognition based on WRFCCF[J]. Journal of Southwest Jiaotong University, 2010, 45(2): 290–295. doi: 10.3969/j.issn.0258-2724.2010.02.022
[4]	黄颖坤, 金炜东, 余志斌, 等. 基于深度学习和集成学习的辐射源信号识别[J]. 系统工程与电子技术, 2018, 40(11): 2420–2425. doi: 10.3969/j.issn.1001-506X.2018.11.05 HUANG Yingkun, JIN Weidong, YU Zhibin, et al. Radar emitter signal recognition based on deep learning and ensemble learning[J]. Systems Engineering and Electronics, 2018, 40(11): 2420–2425. doi: 10.3969/j.issn.1001-506X.2018.11.05
[5]	周志文, 黄高明, 高俊, 等. 一种深度学习的雷达辐射源识别算法[J]. 西安电子科技大学学报:自然科学版, 2017, 44(3): 77–82. doi: 10.3969/j.issn.1001-2400.2017.03.014 ZHOU Zhiwen, HUANG Gaoming, GAO Jun, et al. Radar emitter identification algorithm based on deep learning[J]. Journal of Xidian University, 2017, 44(3): 77–82. doi: 10.3969/j.issn.1001-2400.2017.03.014
[6]	韩俊, 何明浩, 朱振波, 等. 基于复杂度特征的未知雷达辐射源信号分选[J]. 电子与信息学报, 2009, 31(11): 2552–2556. doi: 10.3724/SP.J.1146.2008.01505 HAN Jun, HE Minghao, ZHU Zhenbo, et al. Sorting unknown radar emitter signal based on the complexity characteristics[J]. Journal of Electronics &Information Technology, 2009, 31(11): 2552–2556. doi: 10.3724/SP.J.1146.2008.01505
[7]	普运伟, 金炜东, 朱明, 等. 雷达辐射源信号模糊函数主脊切面特征提取方法[J]. 红外与毫米波学报, 2008, 27(2): 133–137. doi: 10.3321/j.issn:1001-9014.2008.02.012 PU Yunwei, JIN Weidong, ZHU Ming, et al. Extracting the main ridge slice characteristics of ambiguity function for radar emitter signals[J]. Journal of Infrared and Millimeter Waves, 2008, 27(2): 133–137. doi: 10.3321/j.issn:1001-9014.2008.02.012
[8]	陈韬伟, 金炜东. 雷达辐射源信号符号化脉内特征提取方法[J]. 数据采集与处理, 2008, 23(5): 521–526. doi: 10.3969/j.issn.1004-9037.2008.05.004 CHEN Taowei and JIN Weidong. Intra-pulse feature extraction of radar emitter signals based on symbolization method[J]. Journal of Data Acquisition &Processing, 2008, 23(5): 521–526. doi: 10.3969/j.issn.1004-9037.2008.05.004
[9]	黄颖坤, 金炜东, 葛鹏, 等. 基于多尺度信息熵的雷达辐射源信号识别[J]. 电子与信息学报, 2019, 41(5): 1084–1091. doi: 10.11999/JEIT180535 HUANG Yingkun, JIN Weidong, GE Peng, et al. Radar emitter signal identification based on multi-scale information entropy[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1084–1091. doi: 10.11999/JEIT180535
[10]	张瑜, 李玲玲. 多径条件下雷达到达角的估算及仿真[J]. 电波科学学报, 2004, 19(2): 215–218. doi: 10.3969/j.issn.1005-0388.2004.02.018 ZHANG Yu and LI Lingling. Radar arrived angle estimation and simulation under multi-path condition[J]. Chinese Journal of Radio Science, 2004, 19(2): 215–218. doi: 10.3969/j.issn.1005-0388.2004.02.018
[11]	LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
[12]	ZHANG Wei, CUI Xiaodong, FINKLER U, et al. Distributed deep learning strategies for automatic speech recognition[C]. ICASSP 2019 – 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019: 5706–5710.
[13]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 770–778.
[14]	WANG Chao, WANG Jian, and ZHANG Xudong. Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network[C]. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 2017: 2437–2441.
[15]	周东青, 王玉冰, 王星, 等. 基于深度限制波尔兹曼机的辐射源信号识别[J]. 国防科技大学学报, 2016, 38(6): 136–141. doi: 10.11887/j.cn.201606022 ZHOU Dongqing, WANG Yubing, WANG Xing, et al. Radar emitter signal recognition based on deep restricted Boltzmann machine[J]. Journal of National University of Defense Technology, 2016, 38(6): 136–141. doi: 10.11887/j.cn.201606022
[16]	WANG Xuebao, HUANG Gaoming, ZHOU Zhiwen, et al. Radar emitter recognition based on the short time Fourier transform and convolutional neural networks[C]. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 2017: 1–5.
[17]	WANG Yaqing, YAO Quanming, KWOK J T, et al. Generalizing from a few examples: A survey on few-shot learning[J]. ACM Computing Surveys, 2020, 53(3): 63. doi: 10.1145/3386252
[18]	JIANG Wen, HUANG Kai, GENG Jie, et al. Multi-scale metric learning for few-shot learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(3): 1091–1102. doi: 10.1109/TCSVT.2020.2995754
[19]	DAS D and LEE C S G. A two-stage approach to few-shot learning for image recognition[J]. IEEE Transactions on Image Processing, 2019, 29: 3336–3350. doi: 10.1109/TIP.2019.2959254
[20]	LIU Weide, ZHANG Chi, LIN Guosheng, et al. CRNet: Cross-reference networks for few-shot segmentation[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 4164–4172.
[21]	FINN C, ABBEEL P, and LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 2017: 1126–1135.
[22]	HOSPEDALES T M, ANTONIOU A, MICAELLI P, et al. Meta-learning in neural networks: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, To be published.