Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments Article Swipe
D. Li
,
Bin Yao
,
Pu Sun
,
Peitong Li
,
Jianfeng Yan
,
Juzhen Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s13634-024-01138-y
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s13634-024-01138-y
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%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13634-024-01138-y
- https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01138-y
- OA Status
- gold
- Cited By
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- References
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393260295Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13634-024-01138-yDigital Object Identifier
- Title
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Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environmentsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-03-28Full publication date if available
- Authors
-
D. Li, Bin Yao, Pu Sun, Peitong Li, Jianfeng Yan, Juzhen WangList of authors in order
- Landing page
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https://doi.org/10.1186/s13634-024-01138-yPublisher landing page
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https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01138-yDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01138-yDirect OA link when available
- Concepts
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Computer science, Domain (mathematical analysis), Artificial neural network, Identification (biology), Adversarial system, Artificial intelligence, Common emitter, Machine learning, Electronic engineering, Mathematics, Engineering, Biology, Mathematical analysis, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.pdf_url | https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01138-y |
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| primary_location.raw_source_name | EURASIP Journal on Advances in Signal Processing |
| primary_location.landing_page_url | https://doi.org/10.1186/s13634-024-01138-y |
| publication_date | 2024-03-28 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W3038716110, https://openalex.org/W3014127772, https://openalex.org/W3098755434, https://openalex.org/W3205236965, https://openalex.org/W2958312412, https://openalex.org/W3013935691, https://openalex.org/W4285247578, https://openalex.org/W4213377353, https://openalex.org/W1968960382, https://openalex.org/W2304380770, https://openalex.org/W2292953767, https://openalex.org/W2034840140, https://openalex.org/W2096773124, https://openalex.org/W2155886813, https://openalex.org/W2614522447, https://openalex.org/W2649995573, https://openalex.org/W4386432186, https://openalex.org/W4387623697, https://openalex.org/W4386495285, https://openalex.org/W4386078135, https://openalex.org/W4385975859, https://openalex.org/W4312760593, https://openalex.org/W4205348215, https://openalex.org/W3196757423, https://openalex.org/W3146023480, https://openalex.org/W4311771937, https://openalex.org/W4311080746, https://openalex.org/W4312307020, https://openalex.org/W2115403315, https://openalex.org/W2884771968, https://openalex.org/W6637618735, https://openalex.org/W3004205097, https://openalex.org/W2593768305, https://openalex.org/W6600650897, https://openalex.org/W3080327351 |
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