Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors
Classification of drones is important due to their increasing popularity and potential threats. The micro-Doppler signatures that depend on the rotation of rotor blades facilitate the classification of drones. To enhance the robustness of micro-Doppler based classification of drones, dual radar sens...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2018-10-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | http://radars.ie.ac.cn/fileup/HTML/R18061.htm |
| _version_ | 1797426343051264000 |
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| author | Zhang Pengfei Li Gang Huo Chaoying Yin Hongcheng |
| author_facet | Zhang Pengfei Li Gang Huo Chaoying Yin Hongcheng |
| author_sort | Zhang Pengfei |
| collection | DOAJ |
| description | Classification of drones is important due to their increasing popularity and potential threats. The micro-Doppler signatures that depend on the rotation of rotor blades facilitate the classification of drones. To enhance the robustness of micro-Doppler based classification of drones, dual radar sensing classification scheme is proposed in this paper. First, time-frequency spectrograms are obtained by performing a short-time Fourier transform on the radar data collected by two radar sensors that have similar angular diversity. Then, principal components analysis is utilized to extract the features from the time-frequency spectrograms and the features obtained by the two radar sensors are fused together. Finally, the classification results are obtained by using the support vector machine. The experimental results show that the classification accuracy obtained by the fusion of dual radar sensors is 5% higher than that obtained by only using a single radar sensor. |
| first_indexed | 2024-03-09T08:28:51Z |
| format | Article |
| id | doaj.art-7cbe99922dfd4fa587ddd509bf0653f5 |
| institution | Directory Open Access Journal |
| issn | 2095-283X 2095-283X |
| language | English |
| last_indexed | 2024-03-09T08:28:51Z |
| publishDate | 2018-10-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-7cbe99922dfd4fa587ddd509bf0653f52023-12-02T20:30:11ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2095-283X2018-10-017555756410.12000/JR18061Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar SensorsZhang Pengfei0Li Gang1Huo Chaoying2Yin Hongcheng3①(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)①(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)②(Beijing Institute of Environmental Features, Beijing 100854, China)②(Beijing Institute of Environmental Features, Beijing 100854, China)Classification of drones is important due to their increasing popularity and potential threats. The micro-Doppler signatures that depend on the rotation of rotor blades facilitate the classification of drones. To enhance the robustness of micro-Doppler based classification of drones, dual radar sensing classification scheme is proposed in this paper. First, time-frequency spectrograms are obtained by performing a short-time Fourier transform on the radar data collected by two radar sensors that have similar angular diversity. Then, principal components analysis is utilized to extract the features from the time-frequency spectrograms and the features obtained by the two radar sensors are fused together. Finally, the classification results are obtained by using the support vector machine. The experimental results show that the classification accuracy obtained by the fusion of dual radar sensors is 5% higher than that obtained by only using a single radar sensor.http://radars.ie.ac.cn/fileup/HTML/R18061.htmMicro-DopplerDronesTarget classificationMulti-angle and multi-band observation |
| spellingShingle | Zhang Pengfei Li Gang Huo Chaoying Yin Hongcheng Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors Leida xuebao Micro-Doppler Drones Target classification Multi-angle and multi-band observation |
| title | Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors |
| title_full | Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors |
| title_fullStr | Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors |
| title_full_unstemmed | Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors |
| title_short | Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors |
| title_sort | classification of drones based on micro doppler radar signatures using dual radar sensors |
| topic | Micro-Doppler Drones Target classification Multi-angle and multi-band observation |
| url | http://radars.ie.ac.cn/fileup/HTML/R18061.htm |
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