Time-frequency image and high-order spectrum characteristics based radar signal recognition
Aiming at improving the accuracy of radar signal recognition under a low signal-to-noise ratio, a radar signal recognition algorithm based both on time-frequency image and high-order spectrum feature was proposed.Firstly, the time-frequency image was obtained by Choi-Williams distribution (CWD) tran...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-02-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022024/ |
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| _version_ | 1841529021044621312 |
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| author | Shitong LI Daying QUAN Zeyu TANG Yun CHEN Xiaofeng WANG Xiaoping JIN |
| author_facet | Shitong LI Daying QUAN Zeyu TANG Yun CHEN Xiaofeng WANG Xiaoping JIN |
| author_sort | Shitong LI |
| collection | DOAJ |
| description | Aiming at improving the accuracy of radar signal recognition under a low signal-to-noise ratio, a radar signal recognition algorithm based both on time-frequency image and high-order spectrum feature was proposed.Firstly, the time-frequency image was obtained by Choi-Williams distribution (CWD) transform, based on which the time-frequency image was preprocessed and the texture features were extracted by gray level co-occurrence matrix (GLCM) in sequence.Meanwhile, the symmetrical holder coefficient was used to extract the high-order spectral features of the signal.Then, the texture features and high-order spectrum features were form a new set of joint feature vectors.Finally, with the proposed feature vector the classification and recognition of radar signals were implemented by a support vector machine.The algorithm was verified on the data set with eight typical radar signals.Experimental results show that the recognition accuracy of different radar signals can achieve higher than 90% when the signal-to-noise ratio is -8 dB. |
| format | Article |
| id | doaj-art-6dca9cf1734f499bab6429fc25403fd3 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-6dca9cf1734f499bab6429fc25403fd32025-01-15T03:26:42ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-02-0138849159809418Time-frequency image and high-order spectrum characteristics based radar signal recognitionShitong LIDaying QUANZeyu TANGYun CHENXiaofeng WANGXiaoping JINAiming at improving the accuracy of radar signal recognition under a low signal-to-noise ratio, a radar signal recognition algorithm based both on time-frequency image and high-order spectrum feature was proposed.Firstly, the time-frequency image was obtained by Choi-Williams distribution (CWD) transform, based on which the time-frequency image was preprocessed and the texture features were extracted by gray level co-occurrence matrix (GLCM) in sequence.Meanwhile, the symmetrical holder coefficient was used to extract the high-order spectral features of the signal.Then, the texture features and high-order spectrum features were form a new set of joint feature vectors.Finally, with the proposed feature vector the classification and recognition of radar signals were implemented by a support vector machine.The algorithm was verified on the data set with eight typical radar signals.Experimental results show that the recognition accuracy of different radar signals can achieve higher than 90% when the signal-to-noise ratio is -8 dB.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022024/radar signal recognitionhigh order spectrumChoi-Williams time frequency distributionsupport vector machine |
| spellingShingle | Shitong LI Daying QUAN Zeyu TANG Yun CHEN Xiaofeng WANG Xiaoping JIN Time-frequency image and high-order spectrum characteristics based radar signal recognition Dianxin kexue radar signal recognition high order spectrum Choi-Williams time frequency distribution support vector machine |
| title | Time-frequency image and high-order spectrum characteristics based radar signal recognition |
| title_full | Time-frequency image and high-order spectrum characteristics based radar signal recognition |
| title_fullStr | Time-frequency image and high-order spectrum characteristics based radar signal recognition |
| title_full_unstemmed | Time-frequency image and high-order spectrum characteristics based radar signal recognition |
| title_short | Time-frequency image and high-order spectrum characteristics based radar signal recognition |
| title_sort | time frequency image and high order spectrum characteristics based radar signal recognition |
| topic | radar signal recognition high order spectrum Choi-Williams time frequency distribution support vector machine |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022024/ |
| work_keys_str_mv | AT shitongli timefrequencyimageandhighorderspectrumcharacteristicsbasedradarsignalrecognition AT dayingquan timefrequencyimageandhighorderspectrumcharacteristicsbasedradarsignalrecognition AT zeyutang timefrequencyimageandhighorderspectrumcharacteristicsbasedradarsignalrecognition AT yunchen timefrequencyimageandhighorderspectrumcharacteristicsbasedradarsignalrecognition AT xiaofengwang timefrequencyimageandhighorderspectrumcharacteristicsbasedradarsignalrecognition AT xiaopingjin timefrequencyimageandhighorderspectrumcharacteristicsbasedradarsignalrecognition |