Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment
The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing feat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2023-05-01
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| Series: | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/127262/PDF/BPASTS_2023_71_4_3391.pdf |
| _version_ | 1797736103906639872 |
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| author | Wei Ge Lin Qi Lin Tong Jun Zhu Jing Zhang Dongyang Zhao Ke Li |
| author_facet | Wei Ge Lin Qi Lin Tong Jun Zhu Jing Zhang Dongyang Zhao Ke Li |
| author_sort | Wei Ge |
| collection | DOAJ |
| description | The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model. |
| first_indexed | 2024-03-12T13:08:45Z |
| format | Article |
| id | doaj.art-cacacff288e5493887d151525d9e58d8 |
| institution | Directory Open Access Journal |
| issn | 2300-1917 |
| language | English |
| last_indexed | 2024-03-12T13:08:45Z |
| publishDate | 2023-05-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| spelling | doaj.art-cacacff288e5493887d151525d9e58d82023-08-28T09:03:09ZengPolish Academy of SciencesBulletin of the Polish Academy of Sciences: Technical Sciences2300-19172023-05-01714https://doi.org/10.24425/bpasts.2023.145766Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environmentWei Ge0https://orcid.org/0000-0003-0475-3117Lin Qi1Lin Tong2Jun Zhu3Jing Zhang4Dongyang Zhao5Ke Li6https://orcid.org/0000-0001-7279-5015School of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, ChinaSchool of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, ChinaSchool of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, ChinaSchool of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, ChinaSchool of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, ChinaShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, ShenZhen, GuangDong, 518000, ChinaSchool of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, ChinaThe individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model.https://journals.pan.pl/Content/127262/PDF/BPASTS_2023_71_4_3391.pdfcommunication emitter identificationfeature extractiondimensionality reductionvmdesda |
| spellingShingle | Wei Ge Lin Qi Lin Tong Jun Zhu Jing Zhang Dongyang Zhao Ke Li Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment Bulletin of the Polish Academy of Sciences: Technical Sciences communication emitter identification feature extraction dimensionality reduction vmd esda |
| title | Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment |
| title_full | Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment |
| title_fullStr | Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment |
| title_full_unstemmed | Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment |
| title_short | Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment |
| title_sort | research on communication emitter identification based on semi supervised dimensionality reduction in complex electromagnetic environment |
| topic | communication emitter identification feature extraction dimensionality reduction vmd esda |
| url | https://journals.pan.pl/Content/127262/PDF/BPASTS_2023_71_4_3391.pdf |
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