Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite
The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has fai...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/11/1633 |
| _version_ | 1827640681881927680 |
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| author | Yalan Li Jing Yuan Jie Cao Yaohui Liu Jianping Huang Bin Li Qiao Wang Zhourong Zhang Zhixing Zhao Ying Han Haijun Liu Jinsheng Han Xuhui Shen Yali Wang |
| author_facet | Yalan Li Jing Yuan Jie Cao Yaohui Liu Jianping Huang Bin Li Qiao Wang Zhourong Zhang Zhixing Zhao Ying Han Haijun Liu Jinsheng Han Xuhui Shen Yali Wang |
| author_sort | Yalan Li |
| collection | DOAJ |
| description | The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s. |
| first_indexed | 2024-03-09T17:03:02Z |
| format | Article |
| id | doaj.art-f0d7b2b68c7443dfac06131c93dd9465 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T17:03:02Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-f0d7b2b68c7443dfac06131c93dd94652023-11-24T14:28:29ZengMDPI AGAtmosphere2073-44332023-10-011411163310.3390/atmos14111633Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES SatelliteYalan Li0Jing Yuan1Jie Cao2Yaohui Liu3Jianping Huang4Bin Li5Qiao Wang6Zhourong Zhang7Zhixing Zhao8Ying Han9Haijun Liu10Jinsheng Han11Xuhui Shen12Yali Wang13Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, ChinaInstitute of Disaster Prevention, Langfang 065201, ChinaInstitute of Disaster Prevention, Langfang 065201, ChinaHunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, Chenzhou 423000, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, ChinaMicroelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, ChinaHu Nan Giantsun Power Electronics Co., Ltd., Chenzhou 423000, ChinaInstitute of Disaster Prevention, Langfang 065201, ChinaInstitute of Disaster Prevention, Langfang 065201, ChinaInstitute of Disaster Prevention, Langfang 065201, ChinaNational Space Science Center, CAS, Beijing 100085, ChinaInstitute of Disaster Prevention, Langfang 065201, ChinaThe electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s.https://www.mdpi.com/2073-4433/14/11/1633single channel blind source separationlightning whistlerselectric field detectorCSES satellite |
| spellingShingle | Yalan Li Jing Yuan Jie Cao Yaohui Liu Jianping Huang Bin Li Qiao Wang Zhourong Zhang Zhixing Zhao Ying Han Haijun Liu Jinsheng Han Xuhui Shen Yali Wang Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite Atmosphere single channel blind source separation lightning whistlers electric field detector CSES satellite |
| title | Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite |
| title_full | Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite |
| title_fullStr | Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite |
| title_full_unstemmed | Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite |
| title_short | Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite |
| title_sort | spaceborne algorithm for recognizing lightning whistler recorded by an electric field detector onboard the cses satellite |
| topic | single channel blind source separation lightning whistlers electric field detector CSES satellite |
| url | https://www.mdpi.com/2073-4433/14/11/1633 |
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