Research on Radar Signal Sorting based on Ensemble Deep Learning

Authors

Weidong Jin, School of Electrical Engineering, Southwest Jiao tong University, Chengdu 610031, China;
Chunli Chen, School of Electrical Engineering, Southwest Jiao tong University, Chengdu 610031, China;

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.

Recommended Citation

Jin, Weidong and Chen, Chunli (2019) "Research on Radar Signal Sorting based on Ensemble Deep Learning," Journal of System Simulation: Vol. 31: Iss. 9, Article 16.
DOI: 10.16182/j.issn1004731x.joss.17-0313
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss9/16

First Page

1868

Revised Date

2017-09-20

DOI Link

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

Last Page

1874

CLC

TP183

Recommended Citation

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

DOI

10.16182/j.issn1004731x.joss.17-0313