Research on Low Probability of Intercept Radar Signal Recognition Using Deep Belief Network and Bispectra Diagonal Slice

WANG Xing; ZHOU Yipeng; ZHOU Dongqing; CHEN Zhonghui; TIAN Yuanrong

doi:10.11999/JEIT160031

Volume 38 Issue 11

Dec. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2972-2976

WANG Xing, ZHOU Yipeng, ZHOU Dongqing, CHEN Zhonghui, TIAN Yuanrong. 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

Citation:

WANG Xing, ZHOU Yipeng, ZHOU Dongqing, CHEN Zhonghui, TIAN Yuanrong. 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

Citation:

WANG Xing, ZHOU Yipeng, ZHOU Dongqing, CHEN Zhonghui, TIAN Yuanrong. 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

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Research on Low Probability of Intercept Radar Signal Recognition Using Deep Belief Network and Bispectra Diagonal Slice

doi: 10.11999/JEIT160031

1.
(Institute of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi&rsquo
2.
(Unit 95357 of PLA, Foshan 528227, China)

Funds:

The National Natural Science Foundation of China (61372167), The Aeronautical Science Foundation of China (20152096019)

Received Date: 2016-01-16
Rev Recd Date: 2016-07-14
Publish Date: 2016-11-19

Abstract

Abstract

A novel recognition algorithm for Low Probability of Intercept (LPI) radar signal based on deep learning of radar signals Bispectra Diagonal Slice (BDS) is proposed in this paper. Firstly, a Deep Belief Network (DBN) model is established on stacked Restricted Boltzmann Machines (RBM), then the model is used for layer-by-layer unsupervised greedy learning of radar signals BDS. Secondly, a Back Propagation (BP) algorithm is applied to fine tune parameters of DBN model with a supervised way according to learning error. Finally, the BDS-DBN model is constructed to classify and recognize unknown LPI signals. The theoretical analysis and the simulation results show that, the average recognition accuracy of the proposed algorithm for Frequency Modulation Continuous Wave (FMCW), Frank, Costas and FSK/PSK signals can reach 93.4% or ever higher while the SNR is better than 8 dB, which is better than that of Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm and Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) algorithm.
- Low Probability of Intercept (LPI) radar,
- Deep learning,
- Deep Belief Network (DBN),
- Bispectra Diagonal Slice (BDS),
- Restricted Boltzmann Machine (RBM)

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References(21)

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