Research on Radar Signal Sorting based on Ensemble Deep Learning

doi:10.16182/j.issn1004731x.joss.17-0313

Abstract

Abstract: In view of the fact it is difficult to extract the appropriate features quickly and present signal sorting method’s accuracy is low, a signal sorting method based on ensemble deep learning model is proposed. This method stacks different types of deep belief network for radar emitter signal feature learning to improve algorithm. After learning the characteristics of the radar emitter signals deeply, the posterior probability of each model is linearly integrated and learned and the final classification results are determined by the decision layer to further improve the signal recognition rate. The method is used to separate different types of radar emitter simulation signals, and the results show that this method exhibits strong learning ability to nature features. Compared with other methods, it can significantly improve the classification accuracy, meanwhile it verifies the effectiveness and superiority.

Key words: ensemble learning, Deep Belief Networks, signal sorting, linear ensemble

CLC Number:

TP183

Jin Weidong, Chen Chunli. Research on Radar Signal Sorting based on Ensemble Deep Learning[J]. Journal of System Simulation, 2019, 31(9): 1868-1874.

References

[1] Zhu J D, Zhao Y J, Tang J.Automatic Recognition of Radar Signals Based on Time-frequency Image Character[C]. IET International Radar Conference. Xi'an, China: IEEE, 2013: 1-6.
[2] 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.Identification of radar emitter signals based on wavelet packet transform and feature selection[J]. Chinese Journal of circuits and systems, 2006, 11(6): 45-49.
[3] 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: 33-51.
Yu Zhibin.Study on Radar Emitter Signal Identification based on Intra-pulse Features [D]. Chengdu: Sourthwest Jiaotong University, 2010: 33-51.
[4] 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.A deep learning algorithm for radar emitter recognition[J]. Journal of Xi'an Electronic and Science University, 2017, 44(3): 85-90.
[5] Jurgen S.Deep learning in Neural Networks: an Overview[J]. Neural Networks (S0893-6080), 2015, 61: 85-117.
[6] Welling M.A new learning algorithm for mean field Boltzmann machines[C]. Proceedings of the International Conference on Artificial Neural Networks, Madrid, Spain, 2001.
[7] Hinton G E, Osindero S, Teh Y W.A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation (S0899-7667), 2006, 18(7): 1527-1554.
[8] Deng L, Chen J.Sequence classification using the high-level features extracted from deep neural networks[C]. IEEE International Conference on Acoustics: IEEE, 2014.
[9] Deng L, Li J, Huang J T, et al.Recent advances in deep learning for speech research at Microsoft[C]. IEEE International Conference on Acoustics: IEEE, 2013.
[10] Deng L, Hinton G, Kingsbury B.New types of deep neural network learning for speech recognition and related applications: an overview[C]. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing: IEEE, 2013.
[11] Deng L, Yu D, Platt J.Scalable stacking and learning for building deep architectures[C]. IEEE International Conference on Acoustics: IEEE, 2012.
[12] Hinton G, Salakhutdinov R.Reducing the dimensionality of data with neural networks[J]. Science (S0036-8075), 2006, 313(5786): 504-507.
[13] Hinton G, Deng L, Yu D, et al.Deep Neural Networks for Acoustic Modeling in Speech Recognition[J]. IEEE Signal Processing Magazine (S1053-5888), 2012, 29(6): 82-97.
[14] Mohamed A, Dahl G E, Hinton G.Acoustic Modeling Using Deep Belief Networks[J]. IEEE Transactions on Audio, Speech and Language Processing (S1558-7916), 2012, 20(1): 14-22.