TESPOSDA-SEI: tensor embedding substructure preserving open set domain adaptation for specific emitter identification

Specific Emitter Identification (SEI) is an important method for secure authentication of devices in wireless networks. However, the lack of empirical identification models for many unknown devices further affects the speed and accuracy of their authentication. This work aims to propose an unsupervi...

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Bibliographic Details
Published in:Wireless networks 2023-10, Vol.29 (7), p.2935-2951
Main Authors: Wang, Meiyu, Lin, Yun, Liu, Chang, Tian, Qiao, Zha, Haoran, Fu, Jiangzhi
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Authentication
Clustering
Communications Engineering
Computer Communication Networks
Electrical Engineering
Embedding
Emitters
Engineering
Identification
Information technology
IT in Business
Mapping
Mathematical analysis
Networks
Neural networks
Original Paper
Predictions
Tensors
Wireless networks
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Summary:Specific Emitter Identification (SEI) is an important method for secure authentication of devices in wireless networks. However, the lack of empirical identification models for many unknown devices further affects the speed and accuracy of their authentication. This work aims to propose an unsupervised open-set domain adaptation (UOSDA) based method to solve the open-set SEI problem with unknown devices appearing in the test set and few shots in the training set. The basic principle is to learn tensor embedding shared feature space and preserving inter-class substructure, which perform feature space mapping under the joint source and target domain led by mapping error minimize in the source domain. Then, in the shared space, the known and unknown targets are divided by the double clusters method of structure prediction and nearest class prototype. Specifically, this Tensor Embedding Substructure Preserving Open Set Domain Adaptation (TESPOSDA) consists of three parts, tensor substructure based invariant feature learning, unsupervised clustering based on known target intra-class structure prediction and neighbor prediction, UOSDA to refine the predicted labels. Finally, experiments are conducted on the real ADS-B dataset to demonstrate the effectiveness of TESPDA.
ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-023-03317-5