基于多域环境中集合域对抗神经网络的特定发射器识别

IF 1.9 4区 工程技术 Q2 Engineering EURASIP Journal on Advances in Signal Processing Pub Date : 2024-03-28 DOI:10.1186/s13634-024-01138-y
Dingshan Li, Bin Yao, Pu Sun, Peitong Li, Jianfeng Yan, Juzhen Wang
{"title":"基于多域环境中集合域对抗神经网络的特定发射器识别","authors":"Dingshan Li, Bin Yao, Pu Sun, Peitong Li, Jianfeng Yan, Juzhen Wang","doi":"10.1186/s13634-024-01138-y","DOIUrl":null,"url":null,"abstract":"<p>Specific emitter identification is pivotal in both military and civilian sectors for discerning the unique hardware distinctions inherent to various launchers, it can be used to implement security in wireless communications. Recently, a large number of deep learning-based methods for specific emitter identification have been proposed, achieving good performance. However, these methods are trained based on a large amount of data and the data are independently and identically distributed. In actual complex environments, it is very difficult to obtain reliable labeled data. Aiming at the problems of difficulty in data collection and annotation, and the large difference in distribution between training data and test data, a method for individual radiation source identification based on ensemble domain adversarial neural network was proposed. Specifically, a domain adversarial neural network is designed and a Transformer encoder module is added to make the features obey Gaussian distribution and achieve better feature alignment. Ensemble classifiers are then used to enhance the generalization and reliability of the model. In addition, three real and complex migration environments, Alpine–Montane Channel, Plain-Hillock Channel, and Urban-Dense Channel, were constructed, and experiments were conducted on WiFi dataset. The simulation results show that the proposed method exhibits superior performance compared to the other six methods, with an accuracy improvement of about 3%.</p>","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"20 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments\",\"authors\":\"Dingshan Li, Bin Yao, Pu Sun, Peitong Li, Jianfeng Yan, Juzhen Wang\",\"doi\":\"10.1186/s13634-024-01138-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Specific emitter identification is pivotal in both military and civilian sectors for discerning the unique hardware distinctions inherent to various launchers, it can be used to implement security in wireless communications. Recently, a large number of deep learning-based methods for specific emitter identification have been proposed, achieving good performance. However, these methods are trained based on a large amount of data and the data are independently and identically distributed. In actual complex environments, it is very difficult to obtain reliable labeled data. Aiming at the problems of difficulty in data collection and annotation, and the large difference in distribution between training data and test data, a method for individual radiation source identification based on ensemble domain adversarial neural network was proposed. Specifically, a domain adversarial neural network is designed and a Transformer encoder module is added to make the features obey Gaussian distribution and achieve better feature alignment. Ensemble classifiers are then used to enhance the generalization and reliability of the model. In addition, three real and complex migration environments, Alpine–Montane Channel, Plain-Hillock Channel, and Urban-Dense Channel, were constructed, and experiments were conducted on WiFi dataset. The simulation results show that the proposed method exhibits superior performance compared to the other six methods, with an accuracy improvement of about 3%.</p>\",\"PeriodicalId\":11816,\"journal\":{\"name\":\"EURASIP Journal on Advances in Signal Processing\",\"volume\":\"20 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EURASIP Journal on Advances in Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1186/s13634-024-01138-y\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURASIP Journal on Advances in Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s13634-024-01138-y","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

摘要

特定发射器识别在军事和民用领域都至关重要,它可以辨别各种发射器固有的独特硬件区别,还可用于实现无线通信的安全性。最近,人们提出了大量基于深度学习的特定发射器识别方法,并取得了良好的性能。然而,这些方法都是基于大量数据进行训练的,而且数据都是独立且同分布的。在实际的复杂环境中,很难获得可靠的标记数据。针对数据收集和标注困难、训练数据与测试数据的分布差异较大等问题,提出了一种基于集合域对抗神经网络的个体辐射源识别方法。具体来说,设计了一个域对抗神经网络,并添加了一个变压器编码器模块,使特征服从高斯分布,实现更好的特征对齐。然后使用集合分类器来增强模型的泛化和可靠性。此外,还构建了高山-山地通道、平原-丘陵通道和城市-密集通道三种真实而复杂的迁移环境,并在 WiFi 数据集上进行了实验。仿真结果表明,与其他六种方法相比,所提出的方法表现出更优越的性能,准确率提高了约 3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments

Specific emitter identification is pivotal in both military and civilian sectors for discerning the unique hardware distinctions inherent to various launchers, it can be used to implement security in wireless communications. Recently, a large number of deep learning-based methods for specific emitter identification have been proposed, achieving good performance. However, these methods are trained based on a large amount of data and the data are independently and identically distributed. In actual complex environments, it is very difficult to obtain reliable labeled data. Aiming at the problems of difficulty in data collection and annotation, and the large difference in distribution between training data and test data, a method for individual radiation source identification based on ensemble domain adversarial neural network was proposed. Specifically, a domain adversarial neural network is designed and a Transformer encoder module is added to make the features obey Gaussian distribution and achieve better feature alignment. Ensemble classifiers are then used to enhance the generalization and reliability of the model. In addition, three real and complex migration environments, Alpine–Montane Channel, Plain-Hillock Channel, and Urban-Dense Channel, were constructed, and experiments were conducted on WiFi dataset. The simulation results show that the proposed method exhibits superior performance compared to the other six methods, with an accuracy improvement of about 3%.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing 工程技术-工程:电子与电气
CiteScore
3.50
自引率
10.50%
发文量
109
审稿时长
2.6 months
期刊介绍: The aim of the EURASIP Journal on Advances in Signal Processing is to highlight the theoretical and practical aspects of signal processing in new and emerging technologies. The journal is directed as much at the practicing engineer as at the academic researcher. Authors of articles with novel contributions to the theory and/or practice of signal processing are welcome to submit their articles for consideration.
期刊最新文献
Double-layer data-hiding mechanism for ECG signals Maximum radial pattern matching for minimum star map identification Optimized power and speed of Split-Radix, Radix-4 and Radix-2 FFT structures Performance analysis of unconstrained partitioned-block frequency-domain adaptive filters in under-modeling scenarios Maximum length binary sequences and spectral power distribution of periodic signals
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1