基于多源无监督域适应的辐射源个体识别方法

doi:10.3969/j.issn.1006-2475.2025.03.007

摘要/Abstract

摘要： 受传输环境以及辐射源设备工作状态变化的影响，待识别信号与训练信号的信道噪声会有所不同，这会导致训练好的模型识别准确率下降。为了解决这个问题，大多数研究使用了单源无监督域适应方法，将特定噪声下的带标签样本用于待识别目标噪声下无标签样本的学习。一方面，在实际情况中收集的带标签数据可能来自多个源域；另一方面，目标域通常可以看作是多个源域的组合。为了探索基于多源无监督域适应的辐射源个体识别方法，本文提出一种基于原型对齐和对比学习的多源无监督域适应方法，充分学习和利用域内的语义结构信息。首先，使用多个源域和目标域的原型对齐方法来学习组合多个源域的特征表示并设计一个新的伪标签策略。然后，设计一种加权的域内样本到原型的对比学习方法来增加类内的紧凑性和类间的可区分性，对比学习增加了原型对齐的准确性。在公开数据集上的实验结果表明，本文方法在目标域为4 db和8 db的任务中取得了最好的效果，准确率分别为94.1%和97.4%，相比现有的方法分别提高了2.4和1.2个百分点，表明了本文方法的有效性。

关键词: 辐射源个体识别, 多源无监督域适应, 单源无监督域适应, 原型, 对比学习

Abstract: Due to the influence of the transmission environment and the change in the working state of the radiation source equipment， the channel noise of the signal to be identified and the training signal will be different， which will lead to the decrease of the recognition accuracy of the trained model. In order to solve this problem， most studies use single-source unsupervised domain adaptation method to use labeled samples under specific noise for the learning of unlabeled samples under the target noise to be identified. On the one hand， the labeled data collected in the actual situation may come from multiple source domains. On the other hand， the target domain can usually be regarded as a combination of multiple source domains. In order to explore the specific emitter recognition method based on multi-source unsupervised domain adaptation， a multi-source unsupervised domain adaptation method based on prototype alignment and contrast learning is proposed， which fully learns and utilizes the semantic structure information in the domain. Firstly， a prototype alignment method of multiple source domains and target domains is used to learn the feature representation of multiple source domains and a new pseudo-label strategy is designed. Then， this paper designs a weighted intra-domain sample-to-prototype comparative learning method to increase intra-class compactness and inter-class distinguishability. The experimental results on public datasets show that the proposed method achieves the best results in tasks with target domains of 4 db and 8 db， and the accuracy rates are 94.1 % and 97.4 %， respectively， which are 2.4 and 1.2 percentage points higher than the existing methods， indicating the effectiveness of the proposed method.

Key words: specific emitter identification, multi-source unsupervised domain adaptation, single-source unsupervised domain adaptation, prototype, comparative learning

中图分类号:

TP391

张涛涛, 谢钧, 乔平娟. 基于多源无监督域适应的辐射源个体识别方法[J]. 计算机与现代化, 2025, 0(03): 45-51.

ZHANG Taotao, XIE Jun, QIAO Pingjuan. Specific Emitter Identification Method Based on Multi-source Unsupervised Domain Adaptation[J]. Computer and Modernization, 2025, 0(03): 45-51.

参考文献

［1］ TALBOT K I， DULEY P R， HYATT M H. Specific emitter identification and verification［J］. Technology Review， 2003，113：113-130.
［2］桂云川，杨俊安，吕季杰，等. 基于经验模态分解的通信辐射源分形特征提取算法［J］. 探测与控制学报， 2016，38（1）：104-108.
［3］吴子龙，陈红，雷迎科，等. 基于堆栈式 LSTM 网络的通信辐射源个体识别［J］. 系统工程与电子技术， 2020，42（12）：2915-2923.
［4］ WANG J， ZHANG B， ZHANG J， et al. Specific emitter identification based on deep adversarial domain adaptation［C］// 2021 4th International Conference on Information Communication and Signal Processing （ICICSP）. IEEE， 2021：104-109.
［5］ HUANG K J， YANG J， LIU H H， et al. Deep adversarial neural network for specific emitter identification under varying frequency［J］. Bulletin of the Polish Academy of Sciences. Technical Sciences， 2021， 69（2）. DOI： 10.24425
/bpasts.2021.136737.
［6］ ZHANG X L， LI T Y， GONG P， et al. Variable-modulation specific emitter identification with domain adaptation［J］. IEEE Transactions on Information Forensics and Security， 2022，18：380-395.
［7］王聪，但波，张财生. 一种信道自适应的辐射源无监督训练与识别方法［J］. 兵器装备工程学报， 2023，44（4）：211-216.
［8］ WANG W， LI H J， DING Z M， et al. Rethinking maximum mean discrepancy for visual domain adaptation［J］. IEEE Transactions on Neural Networks and Learning Systems， 2021，34（1）：264-277.
［9］ LONG M S， CAO Y， WANG J M， et al. Learning transferable features with deep adaptation networks［C］// Proceedings of the 32nd International Conference on Machine Learning. ACM， 2015：97-105.
［10］ KANG G L， JIANG L， YANG Y， et al. Contrastive adaptation network for unsupervised domain adaptation［C］// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2019：4893-4902.
［11］ LI J J， CHEN E P， DING Z M， et al. Maximum density divergence for domain adaptation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2020，43（11）：3918-3930.
［12］ ZHU Y C， ZHUANG F Z， WANG J D， et al. Deep subdomain adaptation network for image classification［J］. IEEE Transactions on Neural Networks and Learning Systems， 2020，32（4）：1713-1722.
［13］ KRICHEN M. Generative adversarial networks［C］// 2023 14th International Conference on Computing Communication and Networking Technologies （ICCCNT）. ACM， 2023：1-7.
［14］ HASSANPOUR ZONOOZI M， SEYDI V. A survey on adversarial domain adaptation［J］. Neural Processing Letters， 2023，55（3）：2429-2469.
［15］郭梁. 基于迁移学习的辐射源识别技术研究［D］. 成都：电子科技大学， 2021.
［16］ WANG J， ZHANG B， ZHANG J， et al. Specific emitter identification based on deep adversarial domain adaptation［C］// 2021 4th International Conference on Information Communication and Signal Processing （ICICSP）. ACM， 2021：104-109.
［17］ MANSOUR Y， MOHRI M， ROSTAMIZADEH A. Domain adaptation with multiple sources［C］// Proceedings of the 21st International Conference on Neural Information Processing Systems. ACM， 2008：1041-1048.
［18］ HOFFMAN J， MOHRI M， ZHANG N. Algorithms and theory for multiple-source adaptation［C］// Proceedings of the 32nd International Conference on Neural Information Processing Systems. ACM， 2018：8256-8266.
［19］ PENG X C， BAI Q X， XIA X D， et al. Moment matching for multi-source domain adaptation［C］// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. IEEE， 2019：1406-1415.
［20］ ZHAO S C， WANG G Z， ZHANG S H， et al. Multi-source distilling domain adaptation［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2020，34（7）：12975-12983.
［21］ LI Y Y， WANG S S， WANG B L， et al. Transferable feature filtration network for multi-source domain adaptation［J］. Knowledge-Based Systems， 2023，260：110113.
［22］ XU R J， CHEN Z L， ZUO W M， et al. Deep cocktail network： Multi-source unsupervised domain adaptation with category shift［C］// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2018：3964-3973.
［23］ ZHU Y C， ZHUANG F Z， WANG D Q. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources［J］. Proceedings of the AAAI conference on artificial intelligence，2019，33（1）：5989-5996.
［24］ CHEN C Q， XIE W P， HUANG W B， et al. Progressive feature alignment for unsupervised domain adaptation［C］//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE，2019：627-636.
［25］ CHEN T， KORNBLITH S， NOROUZI M， et al. A simple framework for contrastive learning of visual representations［J］. arXiv preprint arXiv：2002.05709， 2020.
［26］ YUE X Y， ZHENG Z W， ZHANG S h， et al. Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation［C］// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2021：13834-13844.
［27］ SANKHE K， BELGIOVINE M， ZHOU F， et al. ORACLE： Optimized radio classification through convolutional neural networks［C］// 2019 IEEE Conference on Computer Communications. IEEE， 2019：370-378.
［28］ AL-SHAWABKA A， RESTUCCIA F， D’ORO S， et al. Massive-scale I/Q datasets for WiFi radio fingerprinting［J］. Computer Networks， 2020，182. DOI： 10.1016/j.comnet.2020.107566.
［29］ HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2016：770-778.
［30］ ZHU Y C， ZHUANG F Z， WANG J D， et al. Multi-representation adaptation network for cross-domain image classification［J］. Neural Networks， 2019，119：214-221.
［31］ WANG Y F， HUANG H Y， RUDIN C， et al. Understanding how dimension reduction tools work： An empirical approach to deciphering t-SNE， UMAP， TriMAP， and PaCMAP for data visualization［J］. Journal of Machine Learning Research， 2021，22（201）：214-221.

[1]	郭晨光, 茅健, 汪云云. 基于语义拓展和嵌入的零样本学习[J]. 计算机与现代化, 2025, 0(02): 19-27.
[2]	樊笛,巨志勇. 基于原型网络的小样本图像识别方法[J]. 计算机与现代化, 2020, 0(03): 103-.
[3]	万一. 基于GIS的公安信息系统建设的研究[J]. 计算机与现代化, 2011, 1(4): 139-141,.
[4]	潘伟;蔡海珊;陈钊. 一种Web应用系统开发平台的研究与设计[J]. 计算机与现代化, 2009, 1(7): 32-35,5.