基于SLIDE+SVM的雷达辐射源信号识别

doi:10.16182/j.issn1004731x.joss.201709010

摘要/Abstract

摘要： 针对依靠经验提取辐射源信号特征方法的不足,提出了一种基于自主特征学习的雷达辐射源信号识别模型。该模型由2个部分组成：(1) 将雷达信号变换到频域,利用改进的分段聚集近似表示(Piecewise Aggregate Approximation,PAA)算法对信号降维; (2) 构造多层线性降噪器(Linear Denoiser,LIDE)进行特征学习,模型采用无监督训练方法,构建一个SVM进行识别。通过仿真5种不同的辐射源信号验证了模型的有效性,结果表明该模型在低信噪比下能获得较好的识别正确率。

关键词: 雷达辐射源信号识别, 分段聚集近似表示, 线性降噪器, 支持向量机

Abstract: For the deficiency of traditional techniques of emitter signal feature extraction which heavily rely on experience, a model of radar emitting signal identification based on feature self-learning was proposed. This model consists of following 2 parts. Firstly, transform radar signal into frequency domain, then reduce signal dimension by using improved Piecewise Aggregate Approximation (PAA) method. Secondly, create the model of multi-layer Liner Denoiser (LIDE) to feature learning by using unsupervised training method. The validity of model was verified by simulating 5 different kinds of emitting signal with the outcome that excellent identification accuracy could be achieved at low SNR levels.

Key words: radar emitter signal identification, piecewise aggregate approximation, liner denoiser, support vector machine

中图分类号:

TN973

黄颖坤, 金炜东. 基于SLIDE+SVM的雷达辐射源信号识别[J]. 系统仿真学报, 2017, 29(9): 1944-1949.

Huang Yingkun, Jin Weidong. Radar Emitter Signal Identification Based on SLIDE+SVM[J]. Journal of System Simulation, 2017, 29(9): 1944-1949.

参考文献

[1] 候印鸣. 综合电子战 [M]. 北京: 国防工业出版社, 2000: 1-32.
(Hou Yinming.Synthetic EW [M]. Beijing, China: National Defense Industry Press, 2000: 1-32.
[2] 余志斌. 基于脉内特征的雷达辐射源信号识别研究[D]. 成都: 西南交通大学, 2010.
(Yu Zhibin.Study on radar emitter signal identification based on intra-pulse features [D]. Chengdu, China: Southwest Jiaotong University, 2010.)
[3] 张国柱. 雷达辐射源识别技术研究 [D]. 长沙: 国防科学技术大学, 2005.
(Zhang Guozhu.Research on emitter identification [D]. Changsha, China: National University of Defense Technology, 2005.)
[4] 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.
(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.)
[5] Zhang X L, You W T, Guo Q, et al.Recognition Method Studies for Radar and Communication Signals Based on Spectral Correlation [C]// International Symposium on Systems and Control in Aeronautics and Astronautics. Piscataway, NJ, USA: IEEE Press, 2010: 363-366.
[6] Ojeda O A Y, Grajal J. CFAR Detectors for Unknown Signals Based on The Spectral Correlation Measurement[C]// IEEE International Radar Conference. Virginia, USA: IEEE, 2005: 944-949.
[7] Tavakoli E T, Falahati A.Radar Signal Recognition by CWD Picture Features[J]. International Journal of Communications Network & System Sciences (S1913-3715), 2012, 5(4): 238-242.
[8] Han S K, Kim H T, Park S H, et al.Efficient Radar Target Recognition Using a Combination of Range Profile and Time-Frequency Analysis[J]. Progress in Electromagnetics Research (S1559-8985), 2010, 108(4): 131-140.
[9] 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, 2017, 44(3): 85-90.
(Zhou Zhiwen, Huang Gaoming, Gao Jun, et al.Radar emitter identification algorithm based on deep learning[J]. Journal of Xidian University, 2017, 44(3): 85-90.)
[10] 袁成. DARPA发展全新的雷达目标识别技术 [EB/OL]. (2015-08-10) [2017-05-18]. http://www.dsti.net/ Information/News/95526. (Yuan C. DARPA Develops a new radar target identification technology [EB/OL]. (2015-08-10) [<date-in-citation content-type="access-date">2017-05-18</date-in-citation>]. http://www.dsti.net/ Information/News/95526
[11] 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.
(Zhou Dongqing, Wang Yubing, Wang Xing, et al.Radar emitter identification algorithm based on deep restricted Boltzmann machine[J]. Journal of National University of Defense Technology, 2016, 38(6): 136-141.)
[12] Vincent P, Larochelle H, Lajoie I, et al.Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion[J]. Journal of Machine Learning Research (S1532-4435), 2010, 11(12): 3371-3408.
[13] Hinton G E, Salakhutdinov R R.Reducing the Dimensionality of Data with Neural Networks[J]. Science (S0036-8075), 2006, 313(5786): 504-507.
[14] Krizhevsky A, Sutskever I, Hinton G E.ImageNet Classification with Deep Convolutional Neural Networks[C]// International Conference on Neural Information Processing Systems. New York, NY, USA: ACM, 2012: 1097-1105.
[15] Liu S, Yang N, Li M, et al.A Recursive Recurrent Neural Network for Statistical Machine Translation[C]// Proceedings of Meeting of the Association for Computational Linguistics, Maryland, USA, USA: The Association for computational Linguistics, 2014: 1491-1500.
[16] Bengio Y, Courville A, Vincent P.Representation Learning: a Review and New Perspectives[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2013, 35(8): 1798-1828.
[17] Lecun Y, Bengio Y, Hinton G.Deep Learning[J]. Nature (S0028-0836), 2015, 521(7553): 436-444.
[18] Xu Z E, Weinberger K Q, Sha F.Rapid Feature Learning with Stacked Linear Denoisers[C]// ICML 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Bellevue, USA, 2011. USA: ICML, 2001.