Polyphase code signal recognition method based on SAMME+ResNet

被引：0
作者：
Sun Y. [1 ]
Tian R. [1 ]
Dong H. [1 ]
Sun L. [2 ]
机构：
[1] School of Aviation Operations and Services, Aviation University of Air Force, Changchun
[2] Unit 93110 of the PLA, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 10期
关键词：
Ensemble learning; Polyphase code; Residual neural network (ResNet); Signal recognition;
D O I：
10.3969/j.issn.1001-506X.2020.10.12
中图分类号：
学科分类号：
摘要：
In view of the characteristics of the traditional polyphase code signal recognition methods, such as low classification accuracy, uneven class recognition rate and non-generality of recognition methods in the case of low signal to noise ratio (SNR), a polyphase code signal recognition method based on stagewise additive modeling using a multi-class exponential loss function (SAMME) algorithm in ensemble learning and residual neural network (ResNet) is proposed. Simulation experiments are carried out to classify and identify five kinds of polyphase code signals, and the validity of the model is verified. The influence of different quantity base learners on the model is analyzed. Finally, the proposed method is compared with the traditional classification methods. Simulation results show that when the SNR is lower than 6 dB, the proposed method improves the classification accuracy by about 10% compared with single residual network and reduces the difference of recognition rate between classes and also has great advantages over common classification methods. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：2239 / 2245
页数：6
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