[1]高敬鹏,孔维宇,刘佳琪,等.基于时频分析的自适应PCA辐射源调制识别[J].应用科技,2018,45(05):33-37.[doi:10.11991/yykj.201712013]
 GAO Jingpeng,KONG Weiyu,LIU Jiaqi,et al.Research on emitter modulation recognition of the adaptive PCA based on time-frequency analysis[J].Applied science and technology,2018,45(05):33-37.[doi:10.11991/yykj.201712013]
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基于时频分析的自适应PCA辐射源调制识别(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第45卷
期数:
2018年05期
页码:
33-37
栏目:
现代电子技术
出版日期:
2018-09-15

文章信息/Info

Title:
Research on emitter modulation recognition of the adaptive PCA based on time-frequency analysis
作者:
高敬鹏12 孔维宇1 刘佳琪2 郜丽鹏1
1. 哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001;
2. 试验物理与计算数学国家级重点实验室, 北京 100076
Author(s):
GAO Jingpeng12 KONG Weiyu1 LIU Jiaqi2 GAO Lipeng1
1. China College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. National Key Laboratory of Science and Technology on Test Physics & Numerical Mathematics, Beijing 100076, China
关键词:
辐射源调制自适应主成分分析不变矩时频分析特征提取支持向量机分类器
Keywords:
emitter modulationadaptiveprincipal component analysismoment invariantstime-frequency analysisfeature extractionsupport vector machinesclassifier
分类号:
TN955.1
DOI:
10.11991/yykj.201712013
文献标志码:
A
摘要:
针对复杂环境非合作通信模式下,识别调制方式运算复杂度高、识别率低的问题,提出一种基于时频分析的自适应特征提取识别算法。该算法结合二阶四阶矩估计法,利用信噪比自适应选取主成分分析特征,通过支持向量机分类器对辐射源调制方式进行识别。仿真结果表明,所提算法识别效果优于其他特征提取识别算法。在信噪比为0 dB时,识别率达到98%以上,较Hu矩和伪Zernike矩有12 dB左右的提升。该算法识别率高、运算量低,有较好的工程应用价值。
Abstract:
Aiming at the problem existing in the modulation recognition such as high computational complexity and low recognition rate in the non-cooperative communication mode of a complex environment, this paper proposes an adaptive feature extraction and recognition algorithm based on time-frequency analysis. The algorithm, which combined with the second-order and fourth-order moment estimation method, uses the signal-to-noise ratio to select the principal component analysis feature adaptively, and identifies the emitter modulation method by the support vector classifier. The simulation results show that the proposed algorithm is superior to other feature extraction algorithms. When the signal-to-noise ratio is 0dB, the recognition rate is over 98%, which is about 12dB higher than that of Hu and pseudo Zernike moments. The algorithm has some advantages of a high recognition rate and a low calculated amount, having good application value in engineering application.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-12-26。
基金项目:国家自然科学基金面上项目(61371099);中央高校基本科研业务费专项项目(HEUCF150814,HEUCFG201832)
作者简介:高敬鹏(1980-),男,讲师,博士后;孔维宇(1990-),男,硕士研究生,硕士
通讯作者:孔维宇,E-mail:kongweiyu@hrbeu.edu.cn
更新日期/Last Update: 2018-09-04