Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map

QU Zhiyu; LI Gen; DENG Zhian

doi:10.11999/JEIT210695

Volume 44 Issue 9

Sep. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 3170-3177

QU Zhiyu, LI Gen, DENG Zhian. Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3170-3177. doi: 10.11999/JEIT210695

Citation:

QU Zhiyu, LI Gen, DENG Zhian. Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3170-3177. doi: 10.11999/JEIT210695

Citation:

PDF( 3425 KB)

Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map

doi: 10.11999/JEIT210695 cstr: 32379.14.JEIT210695

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China

Funds: The National Natural Science Foundation of China (61801143, 61971155)

Received Date: 2021-07-12
Accepted Date: 2022-01-25
Rev Recd Date: 2022-01-18

Available Online: 2022-02-19

Publish Date: 2022-09-19

Abstract

Abstract

In order to solve the problem that traditional radar signal recognition methods can not effectively expand the recognition types, a radar signal recognition method based on knowledge distillation and attention map is proposed. Firstly, the Smooth Pseudo Wigner-Ville Distribution (SPWVD) of the radar signal is used as input; Then, the incremental learning network structure based on residual network is designed, and the loss function based on knowledge distillation and attention map is used to alleviate the catastrophic forgetting in the process of category increment; Finally, a method based on the mean distance of sample features is used to manage the data set, which reduces effectively the occupied storage resources. Experiments show that this method can quickly complete the training of the extended classification signal under the condition of limited storage resources, and has good recognition accuracy for the original classification and the extended classification signal.
- Radar signal recognition,
- Incremental learning,
- Knowledge distillation,
- Attention map,
- Catastrophic forgetting

FullText(HTML)

References(20)

References

[1]	王星, 周一鹏, 周东青, 等. 基于深度置信网络和双谱对角切片的低截获概率雷达信号识别[J]. 电子与信息学报, 2016, 38(11): 2972–2976. doi: 10.11999/JEIT160031 WANG Xing, ZHOU Yipeng, ZHOU Dongqing, et al. Research on low probability of intercept radar signal recognition using deep belief network and bispectra diagonal slice[J]. Journal of Electronics &Information Technology, 2016, 38(11): 2972–2976. doi: 10.11999/JEIT160031
[2]	ZHANG Chunhong, HAN Yuetao, ZHANG Ping, et al. Research on modern radar emitter modelling technique under complex electromagnetic environment[J]. The Journal of Engineering, 2019(20): 7134–7138. doi: 10.1049/joe.2019.0579
[3]	徐玉芬. 现代雷达信号处理的数字脉冲压缩方法[J]. 现代雷达, 2007, 29(7): 61–64. doi: 10.3969/j.issn.1004-7859.2007.07.018 XU Yufen. Methods of digital pulse compression in modern radar signal processing[J]. Modern Radar, 2007, 29(7): 61–64. doi: 10.3969/j.issn.1004-7859.2007.07.018
[4]	曲志昱, 毛校洁, 侯长波. 基于奇异值熵和分形维数的雷达信号识别[J]. 系统工程与电子技术, 2018, 40(2): 303–307. doi: 10.3969/j.issn.1001-506X.2018.02.10 QU Zhiyu, MAO Xiaojie, and HOU Changbo. Radar signal recognition based on singular value entropy and fractal dimension[J]. Systems Engineering and Electronics, 2018, 40(2): 303–307. doi: 10.3969/j.issn.1001-506X.2018.02.10
[5]	肖易寒, 王亮, 郭玉霞. 基于去噪卷积神经网络的雷达信号调制类型识别[J]. 电子与信息学报, 2021, 43(8): 2300–2307. doi: 10.11999/JEIT200506 XIAO Yihan, WANG Liang, and GUO Yuxia. Radar signal modulation type recognition based on denoising convolutional neural network[J]. Journal of Electronics &Information Technology, 2021, 43(8): 2300–2307. doi: 10.11999/JEIT200506
[6]	QU Zhiyu, WANG Wenyang, HOU Changbo, et al. Radar signal intra-pulse modulation recognition based on convolutional denoising autoencoder and deep convolutional neural network[J]. IEEE Access, 2019, 7: 112339–112347. doi: 10.1109/ACCESS.2019.2935247
[7]	郭立民, 寇韵涵, 陈涛, 等. 基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别[J]. 电子与信息学报, 2018, 40(4): 875–881. doi: 10.11999/JEIT170588 GUO Limin, KOU Yunhan, CHEN Tao, et al. Low probability of intercept radar signal recognition based on stacked sparse auto-encoder[J]. Journal of Electronics &Information Technology, 2018, 40(4): 875–881. doi: 10.11999/JEIT170588
[8]	石礼盟, 杨承志, 吴宏超. 基于深层残差网络和三元组损失的雷达信号识别方法[J]. 系统工程与电子技术, 2020, 42(11): 2506–2512. doi: 10.3969/j.issn.1001-506X.2020.11.12 SHI Limeng, YANG Chengzhi, and WU Hongchao. Radar signal recognition method based on deep residual network and triplet loss[J]. Systems Engineering and Electronics, 2020, 42(11): 2506–2512. doi: 10.3969/j.issn.1001-506X.2020.11.12
[9]	PARISI G I, KEMKER R, PART J L, et al. Continual lifelong learning with neural networks: A review[J]. Neural Networks, 2019, 113: 54–71. doi: 10.1016/j.neunet.2019.01.012
[10]	DHAR P, SINGH R V, PENG K C, et al. Learning without memorizing[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 5133–5141.
[11]	HINTON G, VINYALS O, and DEAN J. Distilling the knowledge in a neural network[J]. Computer Science, 2015, 14(7): 38–39.
[12]	SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization[C]. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 618–626.
[13]	ZHOU Bolei, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 2921–2929.
[14]	LI Zhizhong and HOIEM D. Learning without forgetting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(12): 2935–2947. doi: 10.1109/TPAMI.2017.2773081
[15]	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.
[16]	CASTRO F M, MARÍN-JIMÉNEZ M J, GUIL N, et al. End-to-end incremental learning[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 241–257.
[17]	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 (CVPR), Honolulu, USA, 2017: 5533–5542.
[18]	郭立民, 陈鑫, 陈涛. 基于AlexNet模型的雷达信号调制类型识别[J]. 吉林大学学报:工学版, 2019, 49(3): 1000–1008. doi: 10.13229/j.cnki.jdxbgxb20171056 GUO Limin, CHEN Xin, and CHEN Tao. Radar signal modulation type recognition based on AlexNet model[J]. Journal of Jilin University:Engineering and Technology Edition, 2019, 49(3): 1000–1008. doi: 10.13229/j.cnki.jdxbgxb20171056
[19]	秦鑫, 黄洁, 查雄, 等. 基于扩张残差网络的雷达辐射源信号识别[J]. 电子学报, 2020, 48(3): 456–462. doi: 10.3969/j.issn.0372-2112.2020.03.006 QIN Xin, HUANG Jie, ZHA Xiong, et al. Radar emitter signal recognition based on dilated residual network[J]. Acta Electronica Sinica, 2020, 48(3): 456–462. doi: 10.3969/j.issn.0372-2112.2020.03.006
[20]	KÄDING C, RODNER E, FREYTAG A, et al. Fine-tuning deep neural networks in continuous learning scenarios[C]. The 13th Asian Conference on Computer Vision, Taipei, China, 2016: 588–605.