小样本条件下的通信辐射源半监督特征提取

doi:10.3969/j.issn.1001-506X.2020.10.29

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (10): 2381-2389.doi: 10.3969/j.issn.1001-506X.2020.10.29

小样本条件下的通信辐射源半监督特征提取

方章闻¹(), 张金艺^1,²(), 李科²(), 姜玉稀³()

1. 上海大学微电子研究与开发中心, 上海 200444
2. 上海大学特种光纤与光接入网重点实验室, 上海 200444
3. 上海三思系统集成研究所, 上海 201100

收稿日期:2020-01-10 出版日期:2020-10-01 发布日期:2020-09-19
作者简介:方章闻(1995-),男,硕士研究生,主要研究方向为通信辐射源个体特征识别、数字信号处理。E-mail:fangzw95@163.com|张金艺(1965-),男,研究员,博士,主要研究方向为数字信号处理、通信类片上系统设计。E-mail:zhangjinyi@shu.edu.cn|李科(1984-),男,讲师，博士,主要研究方向为信号处理、通信辐射源识别。E-mail:bsblike@163.com|姜玉稀(1977-),男,工程师，博士,主要研究方向为信号处理、智能图像识别。E-mail:jiangyuxi@sansitech.com
基金资助:
十三五国家重点研发计划(2017YFB0403500);上海市教委重点学科资助项目(J50104)

Semi-supervised feature extraction of communication emitter under small sample condition

Zhangwen FANG¹(), Jinyi ZHANG^1,²(), Ke LI²(), Yuxi JIANG³()

1. Microelectronic Research and Development Center, Shanghai University, Shanghai 200444, China
2. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China
3. Shanghai Sansi Institute for System Integration, Shanghai 201100, China

Received:2020-01-10 Online:2020-10-01 Published:2020-09-19

摘要/Abstract

摘要：

在通信辐射源信号有标签样本数量较小的情况下,同类通信辐射源个体信号特征提取困难且识别精度较低。对此,提出了一种小样本条件下的通信辐射源半监督特征提取方法。该方法对少量有标签通信辐射源信号样本以及大量无标签通信辐射源信号样本进行变分模态分解提取高维稳态信息熵,利用指数半监督判别分析法映射信息熵形成个体特征,并通过XGBoost进行通信辐射源个体识别来验证识别效果。实验表明,所提方法识别准确率达到85.33%,相比无监督特征提取方法运算时间降低了76.17%,证明其在同类通信辐射源不同个体识别中具有较好的性能。

关键词: 通信辐射源个体识别, 特征提取, 变分模态分解, 指数半监督判别分析

Abstract:

In the case of few label samples of the communication emitter signal, it is difficult to extract individual features of similar emitter signals and the identification accuracy is low. To this regard, a semi-supervised feature extraction method of communication emitter under the small sample condition is proposed. A small number of labeled emitter signal samples and a large number of unlabeled emitter signal samples are subjected by variational mode decomposition to extract high-dimensional steady-state information entropy. The exponential semi-supervised discriminant analysis is used to map the information entropy to form individual features. In addition, XGBoost is used to identify the communication emitter to verify the identification effect. Experiments show that the proposed method reduces the computation time by 76.17% compared with the unsupervised feature extraction method, and the identification rate reaches 85.33%, which proves that it has better performance in different individual identification of similar communication emitters.

Key words: individual communication emitter identification, feature extraction, variational mode decomposition, exponential semi-supervised discriminant analysis

中图分类号:

TN911.7

方章闻, 张金艺, 李科, 姜玉稀. 小样本条件下的通信辐射源半监督特征提取[J]. 系统工程与电子技术, 2020, 42(10): 2381-2389.

Zhangwen FANG, Jinyi ZHANG, Ke LI, Yuxi JIANG. Semi-supervised feature extraction of communication emitter under small sample condition[J]. Systems Engineering and Electronics, 2020, 42(10): 2381-2389.

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实验方法	识别准确率/%	识别时间/s
①	75.93	6 892.63
②	78.67	2 490.29
③	80.67	1 637.56
④	85.33	1 642.50

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小样本条件下的通信辐射源半监督特征提取

Semi-supervised feature extraction of communication emitter under small sample condition

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