Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment

• Autorzy: Ge Wei , Qi Lin , Tong Lin , Zhu Jun , Zhang Jing , Zhao Dongyang , Li Ke

Identyfikatory

DOI

10.24425/bpasts.2023.145766

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

communication emitter identification; feature extraction; dimensionality reduction; VMD; ESDA; variational mode decomposition; exponential semi-supervised discriminant analysis

PL

identyfikacja emitera komunikacyjnego; ekstrakcja cech; redukcja wymiarowości; VMD; ESDA; rozkład w trybie wariacyjnym; analiza dyskryminacyjna wykładnicza półnadzorowana

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2023
• Tom: Vol. 71, nr 4
• Strony: art. no. e145766
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Ge Wei

• School of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, China
• Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, 230601, China

• https://orcid.org/0000-0003-0475-3117

autor

Qi Lin

• School of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, China
• Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, 230601, China

autor

Tong Lin

• School of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, China
• Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, 230601, China

autor

Zhu Jun

• School of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, China
• Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, 230601, China

autor

Zhang Jing

• School of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, China
• Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, 230601, China

autor

Zhao Dongyang

• Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, ShenZhen, GuangDong, 518000, China

autor

Li Ke

• School of Information & Computer Science, Anhui Agricultural University, Hefei, Anhui, 230036, China
• Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, 230601, China

• https://orcid.org/0000-0001-7279-5015

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).