[1]高鹏成,焦淑红.基于变分自编码器的雷达辐射源个体识别[J].应用科技,2020,47(4):59-65.[doi:10.11991/yykj.201909009]
 GAO Pengcheng,JIAO Shuhong.Radar emitter recognition based on variational autoencoder[J].Applied science and technology,2020,47(4):59-65.[doi:10.11991/yykj.201909009]
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基于变分自编码器的雷达辐射源个体识别(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年4期
页码:
59-65
栏目:
现代电子技术
出版日期:
2020-07-05

文章信息/Info

Title:
Radar emitter recognition based on variational autoencoder
作者:
高鹏成 焦淑红
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
GAO Pengcheng JIAO Shuhong
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
雷达辐射源识别时频变换变分自编码器核PCA支持向量机特征提取图像预处理数据降维
Keywords:
radar emitter recognitiontime-frequency transformationVAEKPCAsupport vector machinefeature extractionimage preprocessingdata dimensionality reduction
分类号:
TN957.51
DOI:
10.11991/yykj.201909009
文献标志码:
A
摘要:
针对雷达辐射源个体识别中特征提取困难和低信噪比下识别率低的问题,从图像角度出发提出了一种基于变分自编码器的雷达辐射源个体识别算法。基于信号时频分析,利用变分自编码器(variational auto-encoder, VAE )提取时频图像的深层特征,并采用核主成分分析(kernel principal component analysis,KPCA)获取特征中的主成分,最后将特征送入支持向量机进行分类识别。仿真结果表明:文中所提算法在识别效率和抗噪声性能等方面均优于其他传统算法。当信噪比(signal-to-noise ratio,SNR)为0 dB时针对6个辐射源进行识别,可获得93%以上的识别率。该算法特征提取简单、系统实时性高,具有较高的工程应用价值。
Abstract:
Aiming at the difficulty of feature extraction and low recognition rate under low signal-to-noise ratio (SNR) in radar emitter individual recognition, this paper proposes a radar emitter individual recognition algorithm based on variational auto-encoder (VAE) from the image point of view. Based on the signal time-frequency analysis, this algorithm extracts the deep features of time-frequency image by using variational self-encoder, and uses kernel principal component analysis (KPCA) to obtain the principal components of the features. Finally, the features are sent to the support vector machine for classification and recognition. The simulation results show that the proposed algorithm is superior to other traditional algorithms in recognition efficiency and anti-noise performance. When the signal-to-noise ratio (SNR) is 0 dB, more than 93% recognition rate can be obtained for six emitters. The algorithm is of simple extraction, high real-time system, and has high engineering application value.

参考文献/References:

[1] RU Xiaohu, LIU Zheng, HUANG Zhitao, et al. Evaluation of unintentional modulation for pulse compression signals based on spectrum asymmetry[J]. IET radar, sonar and navigation, 2017, 11(4): 656–663.
[2] CONNING M, POTGIETER F. Analysis of measured radar data for specific emitter identification[C]//2010 IEEE International Radar Conference. Washington, USA, 2010: 611–617.
[3] RU Xiaohu, LIU Zheng, JIANG Wenli, et al. Recognition performance analysis of instantaneous phase and its transformed features for radar emitter identification[J]. IET radar sonar navigation, 2015, 10(5): 945–952.
[4] IGLESIAS V, GRAJAL J, YESTE-OJEDA O, et al. Real-time radar pulse parameter extractor[C]// 2014 IEEE Radar Conference. Cincinnati, Ohio, USA ,2014: 15–22.
[5] KAWALEC A, OWCZAREK R. Radar emitter recognition using intrapulse data[C]//15th International Conference on Microwaves, Radar and Wireless Communications. Warsaw, Poland, 2004, 2: 435–438.
[6] BERTONCINI C, RUDD K, NOUSAIN B, et al. Wavelet fingerprinting of radio-frequency identification (RFID) tags[J]. IEEE transactions on industrial electronics, 2012, 59(12): 4843–4850.
[7] GUO Haizhao, ZHANG Xiaonu, YANG Libo, et al. Improved fisher linear discriminant analysis for feature extraction of unintentional modulation on pulse by combining ambiguity function with wavelet transform[C]//2016 International Radar Conference, IET. Guangzhou, China, 2016: 115–123.
[8] YUAN Yingjun, HUANG Zhitao, WU Hao, et al. Specific emitter identification based on Hilbert-Huang transform-based time-frequency-energy distribution features[J]. IET communications, 2014, 8(13): 2404–2412.
[9] KANG Naixin, HE Minghao, HAN Jun Han, et al. Radar emitter fingerprint recognition based on bispectrum and SURF feature[C]//2017 IEEE International Conference on Radar. Guangzhou, China, 2017: 71–76.
[10] FANG Cheng, XUE Zhi. Signal classification method based on complete bispectrum and convolutional neural network[J]. Application research of computers, 2018, 35(12): 3766–3769.
[11] LIU Lu, YANG Peiliang, SUN Weiwei, et al. Similar handwritten Chinese character recognition based on CNN-SVM[C]//International Conference on Graphics and Signal Processing. Singapore, Singapore, 2017: 81–87.
[12] 董爱荣. 小波去噪在移动通信中的应用[D]. 大连: 大连海事大学, 2000.
[13] 高敬鹏, 孔维宇, 刘佳琪, 等. 基于时频分析的自适应PCA辐射源调制识别[J]. 应用科技, 2018, 45(5): 33–37
[14] MA Ning, PENG Yu, WANG Shaojun, et al. A weight SAE based hyperspectral image anomaly targets detection[C]//IEEE International Conference on Electronic Measurement and Instruments. Yangzhou, China, 2018: 511–515.
[15] CHAN S C, LEE K C. Radar target identification by kernel principal component analysis on RCS[J]. Journal of electromagnetic waves and applications, 2012, 26(1): 64–74.
[16] JIA Jianhua, YANG Ning, ZHANG Chao. Object-oriented feature selection of high spatial resolution images using an improved Relief algorithm[J]. Mathematical and computer modelling, 2013, 58(3–4): 619–626.
[17] LIAO Kuo, FU Jiansheng, YANG Wanlin. Modified reliefF algorithm for radar HRRP target recognition[J]. Journal of electronic measurement and instrument, 2010, 24(9): 831–836.
[18] ZHANG Yingwei. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM[J]. Chemical engineering science, 2009, 64(5): 801–811.
[19] WANG Dongxing, WANG Huibo, BAN Xiaojuan, et al. An adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network[J]. Sensors , 2019, 19(19): 4320.
[20] 龚永罡, 汤世平. 面向大数据的SVM参数寻优方法[J]. 计算机仿真, 2010, 27(9): 204–207

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备注/Memo

备注/Memo:
收稿日期:2019-09-16。
基金项目:总装预研重点基金项目(61404150101)
作者简介:高鹏成,男,硕士研究生;焦淑红,女,教授,博士生导师
通讯作者:高鹏成,E-mail:gaopengcheng@hrbeu.edu.cn
更新日期/Last Update: 2020-11-27