Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments
Abstract 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 specif...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-03-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-024-01138-y |
| _version_ | 1797233209785712640 |
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| author | Dingshan Li Bin Yao Pu Sun Peitong Li Jianfeng Yan Juzhen Wang |
| author_facet | Dingshan Li Bin Yao Pu Sun Peitong Li Jianfeng Yan Juzhen Wang |
| author_sort | Dingshan Li |
| collection | DOAJ |
| description | Abstract 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%. |
| first_indexed | 2024-04-24T16:12:32Z |
| format | Article |
| id | doaj.art-c355938e3b6648208b8c90b107634729 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-24T16:12:32Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-c355938e3b6648208b8c90b1076347292024-03-31T11:38:16ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802024-03-012024111810.1186/s13634-024-01138-ySpecific emitter identification based on ensemble domain adversarial neural network in multi-domain environmentsDingshan Li0Bin Yao1Pu Sun2Peitong Li3Jianfeng Yan4Juzhen Wang5China Ship Research and Development AcademyChina Ship Research and Development AcademyChina Ship Research and Development AcademyChina Ship Research and Development AcademyChina Ship Research and Development AcademySchool of Electronic Information, Wuhan UniversityAbstract 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%.https://doi.org/10.1186/s13634-024-01138-yDomain adaptationDomain adversarial neural networksEnsemble learningSpecific emitter identificationTransformer encoder |
| spellingShingle | Dingshan Li Bin Yao Pu Sun Peitong Li Jianfeng Yan Juzhen Wang Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments EURASIP Journal on Advances in Signal Processing Domain adaptation Domain adversarial neural networks Ensemble learning Specific emitter identification Transformer encoder |
| title | Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments |
| title_full | Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments |
| title_fullStr | Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments |
| title_full_unstemmed | Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments |
| title_short | Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments |
| title_sort | specific emitter identification based on ensemble domain adversarial neural network in multi domain environments |
| topic | Domain adaptation Domain adversarial neural networks Ensemble learning Specific emitter identification Transformer encoder |
| url | https://doi.org/10.1186/s13634-024-01138-y |
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