Radar emitter signal recognition based on deep restricted Boltzmann machine

被引：8

作者：

Zhou D. ^{[1
]}

Wang Y. ^{[1
]}

Wang X. ^{[1
]}

Cheng X. ^{[2
]}

Xiao J. ^{[3
]}

机构：

[1] Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an

[2] PLA Air Force Xi'an Flight Academy, Xi'an

[3] Equipment Management and Safety Engineering College, Air Force Engineering University, Xi'an

来源：

Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2016年 / 38卷 / 06期

关键词：

Deep learning; Radar emitter signal recognition; Restricted Boltzmann machine;

D O I：

10.11887/j.cn.201606022

中图分类号：

学科分类号：

摘要：

To deal with the problem of radar emitter recognition caused by parameter complexity and agility of muti-function radars in electronic intelligence reconnaissance field, a new recognition model based on deep restricted Boltzmann machine was proposed. The model was composed of multiple restricted Boltzmann machine. A bottom-up hierarchical unsupervised learning was used to obtain the initial parameters, and then the traditional back propagation algorithm was conducted to fine-tune the network parameters, and the Softmax was used to classify the results at last. Simulation and comparison experiment shows that the proposed method has the ability of extracting the parameter features and recognizing the radar emitters, and it has strong robustness as well as high recognition rate. © 2016, NUDT Press. All right reserved.

引用

页码：136 / 141

页数：5

共 17 条

[1]

Dekker A.H., Applying social network analysis concepts to military C<sub>4</sub>ISR architectures, Connections, 24, pp. 93-103, (2002)

[2]

Wang L., Ji H., Li L., Specific emitter recognition based on feature optimization of ambiguity function zero-slice, Journal of Xidian University:Natural Science, 37, 2, pp. 285-289, (2013)

[3]

Dekker A.H., Centralisation and decentralisation in network centric warfare, Journal of Battlefield Technology, 6, 2, pp. 23-28, (2003)

[4]

Wang S., Zhang D., Bi D., Et al., Research on recognizing the radar signal using bispectrum cascade feature, Journal of Xidian University:Natural Science, 39, 2, pp. 127-132, (2012)

[5]

Bengio Y., Delalleau O., On the expressive power of deep architectures, Proceedings of International Conference on Algorithmic Learning Theory, pp. 18-36, (2011)

[6]

Wang S., Jing X., Yang H., Study of isolated speech recognition based on deep learning neural networks, Application Research of Computers, 32, 8, pp. 2289-2291, (2015)

[7]

Hu Z., Fu K., Zhang C., Audio classical composer identification by deep neural network, Journal of Computer Research & Development, 51, 9, pp. 1945-1954, (2013)

[8]

Liang S., Liu Y., Li L., Face recognition under unconstrained based on LBP and deep learning, Journal on Communications, 35, 6, pp. 154-160, (2014)

[9]

Sun Z., Xue L., Xu Y., Marginal fisher feature extraction algorithm based on deep learning, Journal of Electronics & Information Technology, 35, 4, pp. 805-811, (2014)

[10]

Wu J., Guan Y., Lyu X., A deep learning approach in relation extraction in EMRs, Intelligent Computer & Applications, 4, 3, pp. 35-38, (2014)

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