一种深度学习的雷达辐射源识别算法

doi:10.3969/j.issn.1001-2400.2017.03.014

Abstract

Abstract:

Aimed at the deficiency of traditional techniques of radar emitter feature extraction which rely heavily on artificial experience, a novel emitter identification algorithm based on joint deep time-frequency features is proposed. Time-domain signals are transformed into the 2-D time-frequency domain, and dimensionality reduction is implemented with random projection and principal component analysis with respect to sustaining subspace and energy. In the phase of pre-training, the deep model is layer-wise trained with unlabelled samples and network parameters are fine-tuned with label information. Finally the identification task is achieved with a logistic regression classifier. 6 types of emitter signals are adopted in simulation experiments to validate the effectiveness of the proposed algorithm, the experimental results indicating that the joint deep features help to obtain higher identification accuracy and that the algorithm is more efficient.

Key words: time-frequency distribution, dimensionality reduction, stacked auto-encoder, deep learning, radar emitter identification

ZHOU Zhiwen;HUANG Gaoming;GAO Jun;MAN Xin. Radar emitter identification algorithm based on deep learning[J].Journal of Xidian University, 2017, 44(3): 77-82.

References

［1］ LUND N J, TUND N L, KOIVUNEN V. Waveform Recognition in Pulse Compression Radar Systems ［C］//Proceedings of the 2005 IEEE Workshop on Machine Learning for Signal Processing. Piscataway: IEEE, 2005: 271-276.
［2］ MA J, HUANG G M, ZUO W, et al. Robust Radar Waveform Recognition Algorithm Based on Random Projections and Sparse Classification ［J］. IET Radar, Sonar and Navigation, 2014, 8(4): 290-296.
［3］ ZHU J D, ZHAO Y J, TANG J. Automatic Recognition of Radar Signals Based on Time-frequency Image Character ［C］//Proceedings of the IET International Radar Conference. Stevenage: IET, 2013: 1-6.
［4］张葛祥, 荣海娜, 金炜东. 基于小波包变换和特征选择的雷达辐射源信号识别［J］. 电路与系统学报, 2006, 11(6): 45-49.
ZHANG Gexiang, RONG Haina, JIN Weidong. Radar Emitter Signal Recognition Based on Wavelet Packet Transform and Feature Selection ［J］. Journal of Circuits and Systems, 2006, 11(6): 45-49.
［5］司锡才, 柴娟芳. 基于FRFT的α域-包络曲线的雷达信号特征提取及自动分类［J］. 电子与信息学报, 2009, 31(8):1392-1397.
SI Xicai, CHAI Juanfang. Feature Extraction and Auto-sorting to Envelope Function of Rotation Angle α Domain of Radar Signals Based on FRFT ［J］. Journal of Electronics and Information Technology, 2009, 31(8): 1392-1397.
［6］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.
［7］ HINTON G E, SALAKHUTDINOV R R. Reducing the Dimensionality of Data with Neural Networks ［J］. Science, 2006, 313(5786): 504-507.
［8］ HINTON G, DENG L, YU D, et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition: the Shared Views of Four Research Groups ［J］. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.
［9］ CHEN Y S, LIN Z H, ZHAO X, et al. Deep Learning-based Classification of Hyperspectral Data ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094-2107.
［10］ SCHMIDHUBER J. Deep Learning in Neural Networks: an Overview ［J］. Neural Networks, 2015, 61: 85-117.
［11］孙志军, 薛磊, 许阳明. 基于深度学习的边际Fisher分析特征提取算法［J］.电子与信息学报, 2013, 35(4): 805-811.
SUN Zhijun, XUE Lei, XU Yangming. Marginal Fisher Feature Extraction Algorithm Based on Deep Learning ［J］. Journal of Electronics and Information Technology, 2013, 35(4): 805-811.
［12］ DING C, TAO D. Robust Face Recognition via Multimodal Deep Face Representation ［J］. IEEE Transactions on Multimedia, 2015, 17(11): 2049-2058.
［13］ THAYANANTHAN T, STANKOVIC L, AMIN M, et al. Time-frequency Approach to Radar Detection, Imaging, and Classification ［J］. IET Signal Processing, 2010, 4(4): 325-328.
［14］ XIE H M, WANG S, LIU K, et al. Multilayer Feature Learning for Polarimetric Synthetic Radar Data Classification ［C］//Proceedings of the International Geoscience and Remote Sensing Symposium. Piscataway: IEEE, 2014: 2818-2821.
［15］白航, 赵拥军, 胡德秀. 时频图像局部二值模式特征在雷达信号分类识别中的应用［J］. 宇航学报, 2013, 34(1): 139-146.
BAI Hang, ZHAO Yongjun, HU Dexiu. Radar Signal Recognition Based on the Local Binary Pattern Feature of Time-frequency Image ［J］. Journal of Astronautics, 2013, 34(1): 139-146.
［16］史亚, 姬红兵, 朱明哲, 等. 多核融合框架下的雷达辐射源个体识别［J］. 电子与信息学报, 2014, 36(10): 2484-2490.
SHI Ya, JI Hongbing, ZHU Mingzhe, et al. Specific Radar Emitter Identification in Multiple Kernel Fusion Framework ［J］. Journal of Electronics and Information Technology, 2014, 36(10): 2484-2490.

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Radar emitter identification algorithm based on deep learning

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