An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals
With the rapid development of communication and information technology, it is difficult for traditional signal detection and recognition methods to accurately acquire and identify the intelligence under complex environments. In order to solve this problem, this paper proposes a subtle feature extrac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/8/2/246 |
| _version_ | 1811187982612299776 |
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| author | Jingchao Li Dongyuan Bi Yulong Ying Kai Wei Bin Zhang |
| author_facet | Jingchao Li Dongyuan Bi Yulong Ying Kai Wei Bin Zhang |
| author_sort | Jingchao Li |
| collection | DOAJ |
| description | With the rapid development of communication and information technology, it is difficult for traditional signal detection and recognition methods to accurately acquire and identify the intelligence under complex environments. In order to solve this problem, this paper proposes a subtle feature extraction and recognition algorithm for radiation source individual signals based on multidimensional hybrid features. Firstly, Hilbert transform was performed on the radiation source signals from 10 identical radio devices, and the subtle features of different radiation sources’ signals were extracted. Then, traditional principal component analysis (PCA) algorithm was used to extract and reduce the principal components of the extracted feature data sets. Aiming at the insufficiency of traditional PCA algorithm, an improved principal component analysis algorithm was proposed. At last, a gray relation algorithm was used to classify and identify the radiation source individual signals, and the recognition rate was calculated. Experimental results show that Hilbert transform combined with the improved PCA algorithm can achieve a recognition rate of 99.67% for the "fingerprint" features of radiation source individual signals under the signal-to-noise ratio (SNR) of 20 dB. Compared with the traditional algorithms, the recognition rate increased by 5.67%. Therefore, it provides a powerful theoretical basis for extracting subtle features of radiation source devices under complex electromagnetic environments. |
| first_indexed | 2024-04-11T14:11:46Z |
| format | Article |
| id | doaj.art-c1cdb15bcf8a46f886af4b8aab2bf135 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T14:11:46Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c1cdb15bcf8a46f886af4b8aab2bf1352022-12-22T04:19:41ZengMDPI AGElectronics2079-92922019-02-018224610.3390/electronics8020246electronics8020246An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual SignalsJingchao Li0Dongyuan Bi1Yulong Ying2Kai Wei3Bin Zhang4School of Electronic and Information, Shanghai Dianji University, Shanghai 201306, ChinaSchool of Electronic and Information, Shanghai Dianji University, Shanghai 201306, ChinaSchool of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaSchool of Electronic and Information, Shanghai Dianji University, Shanghai 201306, ChinaDepartment of Mechanical Engineering, Kanagawa University, Yokohama 221-8686, JapanWith the rapid development of communication and information technology, it is difficult for traditional signal detection and recognition methods to accurately acquire and identify the intelligence under complex environments. In order to solve this problem, this paper proposes a subtle feature extraction and recognition algorithm for radiation source individual signals based on multidimensional hybrid features. Firstly, Hilbert transform was performed on the radiation source signals from 10 identical radio devices, and the subtle features of different radiation sources’ signals were extracted. Then, traditional principal component analysis (PCA) algorithm was used to extract and reduce the principal components of the extracted feature data sets. Aiming at the insufficiency of traditional PCA algorithm, an improved principal component analysis algorithm was proposed. At last, a gray relation algorithm was used to classify and identify the radiation source individual signals, and the recognition rate was calculated. Experimental results show that Hilbert transform combined with the improved PCA algorithm can achieve a recognition rate of 99.67% for the "fingerprint" features of radiation source individual signals under the signal-to-noise ratio (SNR) of 20 dB. Compared with the traditional algorithms, the recognition rate increased by 5.67%. Therefore, it provides a powerful theoretical basis for extracting subtle features of radiation source devices under complex electromagnetic environments.https://www.mdpi.com/2079-9292/8/2/246subtle feature extractionsignal recognitionHilbert transformimproved PCA algorithmgray relation classifier |
| spellingShingle | Jingchao Li Dongyuan Bi Yulong Ying Kai Wei Bin Zhang An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals Electronics subtle feature extraction signal recognition Hilbert transform improved PCA algorithm gray relation classifier |
| title | An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals |
| title_full | An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals |
| title_fullStr | An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals |
| title_full_unstemmed | An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals |
| title_short | An Improved Algorithm for Extracting Subtle Features of Radiation Source Individual Signals |
| title_sort | improved algorithm for extracting subtle features of radiation source individual signals |
| topic | subtle feature extraction signal recognition Hilbert transform improved PCA algorithm gray relation classifier |
| url | https://www.mdpi.com/2079-9292/8/2/246 |
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