Detection and identification technology of rotor unmanned aerial vehicles in 5G scene
In the 5G era, integration between different networks is required to realize a new world of Internet of things, the most typical model is Space–Air–Ground Internet of things. In the Space–Air–Ground Internet of things, unmanned aerial vehicle network is widely used as the representative of air-based...
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| Format: | Article |
| Language: | English |
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Wiley
2019-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719853990 |
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| _version_ | 1832547223643095040 |
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| author | Fengtong Xu Tao Hong Jingcheng Zhao Tao Yang |
| author_facet | Fengtong Xu Tao Hong Jingcheng Zhao Tao Yang |
| author_sort | Fengtong Xu |
| collection | DOAJ |
| description | In the 5G era, integration between different networks is required to realize a new world of Internet of things, the most typical model is Space–Air–Ground Internet of things. In the Space–Air–Ground Internet of things, unmanned aerial vehicle network is widely used as the representative of air-based networks. Therefore, a lot of unmanned aerial vehicle “black flying” incidents have occurred. UAVs are a kind of “low, slow and small” artificial targets, which face enormous challenges in detecting, identifying, and managing them. In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. For a one-rotor unmanned aerial vehicle, the radar echo data are a single-component sinusoidal frequency modulation signal. The echo signal is conjugated first and then is subjected to a short-time Fourier transform, while the micro-Doppler has a double effect. For a multi-rotor unmanned aerial vehicle, the radar echo data are a multi-component sinusoidal frequency modulation signal, the k -order Bessel function base and the signal are used for integral projection processing, which better identifies the micro-Doppler characteristics such as the number of rotors or the rotational speed of each rotor. The noise interference is added to verify that the algorithm has better robustness. The micro-Doppler characteristics of rotor unmanned aerial vehicles are extracted by the above algorithm, and the data sets are built to train the model. Finally, the classification of unmanned aerial vehicle is realized, and the classification results are given. The research in this article provides an effective solution to solve the problem of detecting and identifying unmanned aerial vehicle by 5G millimeter wave radar in the Internet of Things, which has high practical application value. |
| format | Article |
| id | doaj-art-6394aeddb1204d2485b77832376d015a |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-6394aeddb1204d2485b77832376d015a2025-02-03T06:45:37ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-06-011510.1177/1550147719853990Detection and identification technology of rotor unmanned aerial vehicles in 5G sceneFengtong XuTao HongJingcheng ZhaoTao YangIn the 5G era, integration between different networks is required to realize a new world of Internet of things, the most typical model is Space–Air–Ground Internet of things. In the Space–Air–Ground Internet of things, unmanned aerial vehicle network is widely used as the representative of air-based networks. Therefore, a lot of unmanned aerial vehicle “black flying” incidents have occurred. UAVs are a kind of “low, slow and small” artificial targets, which face enormous challenges in detecting, identifying, and managing them. In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. For a one-rotor unmanned aerial vehicle, the radar echo data are a single-component sinusoidal frequency modulation signal. The echo signal is conjugated first and then is subjected to a short-time Fourier transform, while the micro-Doppler has a double effect. For a multi-rotor unmanned aerial vehicle, the radar echo data are a multi-component sinusoidal frequency modulation signal, the k -order Bessel function base and the signal are used for integral projection processing, which better identifies the micro-Doppler characteristics such as the number of rotors or the rotational speed of each rotor. The noise interference is added to verify that the algorithm has better robustness. The micro-Doppler characteristics of rotor unmanned aerial vehicles are extracted by the above algorithm, and the data sets are built to train the model. Finally, the classification of unmanned aerial vehicle is realized, and the classification results are given. The research in this article provides an effective solution to solve the problem of detecting and identifying unmanned aerial vehicle by 5G millimeter wave radar in the Internet of Things, which has high practical application value.https://doi.org/10.1177/1550147719853990 |
| spellingShingle | Fengtong Xu Tao Hong Jingcheng Zhao Tao Yang Detection and identification technology of rotor unmanned aerial vehicles in 5G scene International Journal of Distributed Sensor Networks |
| title | Detection and identification technology of rotor unmanned aerial vehicles in 5G scene |
| title_full | Detection and identification technology of rotor unmanned aerial vehicles in 5G scene |
| title_fullStr | Detection and identification technology of rotor unmanned aerial vehicles in 5G scene |
| title_full_unstemmed | Detection and identification technology of rotor unmanned aerial vehicles in 5G scene |
| title_short | Detection and identification technology of rotor unmanned aerial vehicles in 5G scene |
| title_sort | detection and identification technology of rotor unmanned aerial vehicles in 5g scene |
| url | https://doi.org/10.1177/1550147719853990 |
| work_keys_str_mv | AT fengtongxu detectionandidentificationtechnologyofrotorunmannedaerialvehiclesin5gscene AT taohong detectionandidentificationtechnologyofrotorunmannedaerialvehiclesin5gscene AT jingchengzhao detectionandidentificationtechnologyofrotorunmannedaerialvehiclesin5gscene AT taoyang detectionandidentificationtechnologyofrotorunmannedaerialvehiclesin5gscene |