Deep adversarial neural network for specific emitter identification under varying frequency

• Autorzy: Huang Keju , Yang Junan , Liu Hui , Hu Pengjiang

Identyfikatory

DOI

10.24425/bpasts.2021.136737

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

specific emitter identification; unsupervised domain adaptation; transfer learning; deep learning

PL

identyfikacja emitera konkretna; adaptacja domeny nienadzorowana; transfer uczenia się; uczenie głębokie

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2021
• Tom: Vol. 69, nr 2
• Strony: art. no. e136737
• Opis fizyczny: Bibliogr. 46 poz., rys., tab.

Twórcy

autor

Huang Keju

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China

autor

Yang Junan

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China

autor

Liu Hui

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China

autor

Hu Pengjiang

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).