Improved SVM Communication Signal Recognition Based on Information Geometry Denoising
Aiming the problem of low accuracy of communication signal recognition by traditional manual feature extraction, an improved SVM recognition method based on information geometry denoising is proposed exploiting the support vector machine (SVM). The proposed method obtains the time-frequency images o...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Aero Weaponry
2023-10-01
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| Series: | Hangkong bingqi |
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| Online Access: | https://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2023-00003.pdf |
| _version_ | 1797387884000444416 |
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| author | Cheng Yuqing, Guo Muran, Wang Leping |
| author_facet | Cheng Yuqing, Guo Muran, Wang Leping |
| author_sort | Cheng Yuqing, Guo Muran, Wang Leping |
| collection | DOAJ |
| description | Aiming the problem of low accuracy of communication signal recognition by traditional manual feature extraction, an improved SVM recognition method based on information geometry denoising is proposed exploiting the support vector machine (SVM). The proposed method obtains the time-frequency images of different communication signals through the Choi-Williams distribution (CWD) time-frequency transform, and uses the geometric ground distance to accurately measure the difference between pixels for denoising. Then, the AlexNet is used to extract features from the time-frequency maps. Finally, by using the improved SVM based on the information geometry, the classification of communication signal is made to achieve effective classification and recognition. The simulation results show that the recognition rate of the proposed method achieves more than 97% at 0 dB signal-to-noise ratio (SNR). In addition, the method is still effective in the case of small samples. |
| first_indexed | 2024-03-08T22:32:33Z |
| format | Article |
| id | doaj.art-ac6e6317da584188832ded6c233f10f3 |
| institution | Directory Open Access Journal |
| issn | 1673-5048 |
| language | zho |
| last_indexed | 2024-03-08T22:32:33Z |
| publishDate | 2023-10-01 |
| publisher | Editorial Office of Aero Weaponry |
| record_format | Article |
| series | Hangkong bingqi |
| spelling | doaj.art-ac6e6317da584188832ded6c233f10f32023-12-18T01:02:00ZzhoEditorial Office of Aero WeaponryHangkong bingqi1673-50482023-10-0130512112610.12132/ISSN.1673-5048.2023.0003Improved SVM Communication Signal Recognition Based on Information Geometry DenoisingCheng Yuqing, Guo Muran, Wang Leping01. College of Information and Communication Engineering, Harbin Engineering University, Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China;2. College of Communication Engineering, Army Engineering University of PLA, Nanjing 210000, ChinaAiming the problem of low accuracy of communication signal recognition by traditional manual feature extraction, an improved SVM recognition method based on information geometry denoising is proposed exploiting the support vector machine (SVM). The proposed method obtains the time-frequency images of different communication signals through the Choi-Williams distribution (CWD) time-frequency transform, and uses the geometric ground distance to accurately measure the difference between pixels for denoising. Then, the AlexNet is used to extract features from the time-frequency maps. Finally, by using the improved SVM based on the information geometry, the classification of communication signal is made to achieve effective classification and recognition. The simulation results show that the recognition rate of the proposed method achieves more than 97% at 0 dB signal-to-noise ratio (SNR). In addition, the method is still effective in the case of small samples.https://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2023-00003.pdf|information geometry|image denoising|communication signal|modulation identification|support vector machine(svm)|ground distance measurement|alexnet |
| spellingShingle | Cheng Yuqing, Guo Muran, Wang Leping Improved SVM Communication Signal Recognition Based on Information Geometry Denoising Hangkong bingqi |information geometry|image denoising|communication signal|modulation identification|support vector machine(svm)|ground distance measurement|alexnet |
| title | Improved SVM Communication Signal Recognition Based on Information Geometry Denoising |
| title_full | Improved SVM Communication Signal Recognition Based on Information Geometry Denoising |
| title_fullStr | Improved SVM Communication Signal Recognition Based on Information Geometry Denoising |
| title_full_unstemmed | Improved SVM Communication Signal Recognition Based on Information Geometry Denoising |
| title_short | Improved SVM Communication Signal Recognition Based on Information Geometry Denoising |
| title_sort | improved svm communication signal recognition based on information geometry denoising |
| topic | |information geometry|image denoising|communication signal|modulation identification|support vector machine(svm)|ground distance measurement|alexnet |
| url | https://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2023-00003.pdf |
| work_keys_str_mv | AT chengyuqingguomuranwangleping improvedsvmcommunicationsignalrecognitionbasedoninformationgeometrydenoising |