A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm
The rapid development of civil UAV promotes the social and economic development, and the frequent “flying illegally” events has brought great challenges to aviation safety and government supervision. The frequency hopping communication system used in UAV data transmission and c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9393344/ |
| _version_ | 1818611397227970560 |
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| author | Jiaquan Ye Jie Zou Jing Gao Guomin Zhang Mingming Kong Zheng Pei Kaitao Cui |
| author_facet | Jiaquan Ye Jie Zou Jing Gao Guomin Zhang Mingming Kong Zheng Pei Kaitao Cui |
| author_sort | Jiaquan Ye |
| collection | DOAJ |
| description | The rapid development of civil UAV promotes the social and economic development, and the frequent “flying illegally” events has brought great challenges to aviation safety and government supervision. The frequency hopping communication system used in UAV data transmission and control link has the advantages of anti-jamming and anti-interception, and its complex electromagnetic environment, which also brings great difficulties to UAV detection. In this paper, the detection of civil UAV is realized by frequency hopping signal monitoring. Firstly, by analyzing the signal characteristics of UAVs, an adaptive noise threshold calculation method is proposed for find the signals from spectrum data. Then, the improved clustering analysis algorithm is proposed based on constructed the waveform shape characteristics and peak characteristics of UAV frequency hopping signal. Finally, according to the designed experimental process, the experimental environment is set up, and the UAV monitoring, discovery and parameter estimation are realized by using the improved clustering analysis algorithm, and compared with K-means, K-means<sup>++</sup>, DBSCAN, Multi-hop and Auto-correlation methods. The results show that the method has certain robustness and has a good application prospect. |
| first_indexed | 2024-12-16T15:29:41Z |
| format | Article |
| id | doaj.art-44ca66fba5a649c2809696c7acde42aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T15:29:41Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-44ca66fba5a649c2809696c7acde42aa2022-12-21T22:26:23ZengIEEEIEEE Access2169-35362021-01-019531905320410.1109/ACCESS.2021.30704919393344A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering AlgorithmJiaquan Ye0Jie Zou1Jing Gao2Guomin Zhang3Mingming Kong4https://orcid.org/0000-0003-2869-5441Zheng Pei5https://orcid.org/0000-0001-5757-6607Kaitao Cui6The Second Research Institute of CAAC, Chengdu, ChinaThe Second Research Institute of CAAC, Chengdu, ChinaThe Second Research Institute of CAAC, Chengdu, ChinaCollege of Computer Science and Software Engineering, Xihua University, Chengdu, ChinaCollege of Computer Science and Software Engineering, Xihua University, Chengdu, ChinaCollege of Computer Science and Software Engineering, Xihua University, Chengdu, ChinaThe Second Research Institute of CAAC, Chengdu, ChinaThe rapid development of civil UAV promotes the social and economic development, and the frequent “flying illegally” events has brought great challenges to aviation safety and government supervision. The frequency hopping communication system used in UAV data transmission and control link has the advantages of anti-jamming and anti-interception, and its complex electromagnetic environment, which also brings great difficulties to UAV detection. In this paper, the detection of civil UAV is realized by frequency hopping signal monitoring. Firstly, by analyzing the signal characteristics of UAVs, an adaptive noise threshold calculation method is proposed for find the signals from spectrum data. Then, the improved clustering analysis algorithm is proposed based on constructed the waveform shape characteristics and peak characteristics of UAV frequency hopping signal. Finally, according to the designed experimental process, the experimental environment is set up, and the UAV monitoring, discovery and parameter estimation are realized by using the improved clustering analysis algorithm, and compared with K-means, K-means<sup>++</sup>, DBSCAN, Multi-hop and Auto-correlation methods. The results show that the method has certain robustness and has a good application prospect.https://ieeexplore.ieee.org/document/9393344/Civil UAVhopping signal detectionclustering |
| spellingShingle | Jiaquan Ye Jie Zou Jing Gao Guomin Zhang Mingming Kong Zheng Pei Kaitao Cui A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm IEEE Access Civil UAV hopping signal detection clustering |
| title | A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm |
| title_full | A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm |
| title_fullStr | A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm |
| title_full_unstemmed | A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm |
| title_short | A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm |
| title_sort | new frequency hopping signal detection of civil uav based on improved k means clustering algorithm |
| topic | Civil UAV hopping signal detection clustering |
| url | https://ieeexplore.ieee.org/document/9393344/ |
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