Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar
Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/4/658 |
| _version_ | 1797570491811102720 |
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| author | Mingzhe Zhu Zhenpeng Feng Xianda Zhou Rui Xiao Yue Qi Xinliang Zhang |
| author_facet | Mingzhe Zhu Zhenpeng Feng Xianda Zhou Rui Xiao Yue Qi Xinliang Zhang |
| author_sort | Mingzhe Zhu |
| collection | DOAJ |
| description | Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning. |
| first_indexed | 2024-03-10T20:25:20Z |
| format | Article |
| id | doaj.art-9623b4291ec8481093bc454f94739e2f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T20:25:20Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-9623b4291ec8481093bc454f94739e2f2023-11-19T21:51:01ZengMDPI AGElectronics2079-92922020-04-019465810.3390/electronics9040658Specific Emitter Identification Based on Synchrosqueezing Transform for Civil RadarMingzhe Zhu0Zhenpeng Feng1Xianda Zhou2Rui Xiao3Yue Qi4Xinliang Zhang5School of Electronic Engineering, Xidian University, Xi’an 710121, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710121, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710121, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710121, ChinaDepartment of Electrical and Computer Engineering, Villanova University, 800 Lancaster Ave, Villanova, PA 19085, USADepartment of Electrical and Computer Engineering, Villanova University, 800 Lancaster Ave, Villanova, PA 19085, USATime-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning.https://www.mdpi.com/2079-9292/9/4/658linear relevance propagationspecific emitter identificationsynchrosqueezing transformtime-frequency analysis |
| spellingShingle | Mingzhe Zhu Zhenpeng Feng Xianda Zhou Rui Xiao Yue Qi Xinliang Zhang Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar Electronics linear relevance propagation specific emitter identification synchrosqueezing transform time-frequency analysis |
| title | Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar |
| title_full | Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar |
| title_fullStr | Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar |
| title_full_unstemmed | Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar |
| title_short | Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar |
| title_sort | specific emitter identification based on synchrosqueezing transform for civil radar |
| topic | linear relevance propagation specific emitter identification synchrosqueezing transform time-frequency analysis |
| url | https://www.mdpi.com/2079-9292/9/4/658 |
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