- Tytuł:
- Deep adversarial neural network for specific emitter identification under varying frequency
- Autorzy:
-
Huang, Keju
Yang, Junan
Liu, Hui
Hu, Pengjiang - Powiązania:
- https://bibliotekanauki.pl/articles/2173603.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
specific emitter identification
unsupervised domain adaptation
transfer learning
deep learning
identyfikacja emitera konkretna
adaptacja domeny nienadzorowana
transfer uczenia się
uczenie głębokie - Opis:
- Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; art. no. e136737
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł