Specific emitter identification based on one-dimensional complex-valued residual networks with an attention mechanism

• Autorzy: Qu Lingzhi , Yang Junan , Huang Keju , Liu Hui

Identyfikatory

DOI

10.24425/bpasts.2021.138814

• Języki publikacji: EN

Abstrakty

EN

Specific emitter identification (SEI) can distinguish single-radio transmitters using the subtle features of the received waveform. Therefore, it is used extensively in both military and civilian fields. However, the traditional identification method requires extensive prior knowledge and is time-consuming. Furthermore, it imposes various effects associated with identifying the communication radiation source signal in complex environments. To solve the problem of the weak robustness of the hand-crafted feature method, many scholars at home and abroad have used deep learning for image identification in the field of radiation source identification. However, the classification method based on a real-numbered neural network cannot extract In-phase/Quadrature (I/Q)-related information from electromagnetic signals. To address these shortcomings, this paper proposes a new SEI framework for deep learning structures. In the proposed framework, a complex-valued residual network structure is first used to mine the relevant information between the in-phase and orthogonal components of the radio frequency baseband signal. Then, a one-dimensional convolution layer is used to a) directly extract the features of a specific one-dimensional time-domain signal sequence, b) use the attention mechanism unit to identify the extracted features, and c) weight them according to their importance. Experiments show that the proposed framework having complex-valued residual networks with attention mechanism has the advantages of high accuracy and superior performance in identifying communication radiation source signals.

Słowa kluczowe

EN

complex-valued residual network; specific emitter identification; fingerprint characteristic; attention mechanism; one-dimensional convolution

PL

sieć rezydualna o złożonej wartości; specyficzna identyfikacja emiterów; charakterystyka linii papilarnych; mechanizm uwagi; splot jednowymiarowy

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2021
• Tom: Vol. 69, nr 5
• Strony: art. no. e138814
• Opis fizyczny: Bibliogr. 40 poz., rys., tab.

Twórcy

autor

Qu Lingzhi

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China

autor

Yang Junan

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China

autor

Huang Keju

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China

autor

Liu Hui

• College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China

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