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 control link...

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Vydané v:IEEE access Ročník 9; s. 53190 - 53204
Hlavní autori: Ye, Jiaquan, Zou, Jie, Gao, Jing, Zhang, Guomin, Kong, Mingming, Pei, Zheng, Cui, Kaitao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Air safety
Algorithms
Bandwidth
Civil UAV
Cluster analysis
Clustering
Clustering algorithms
Communications systems
Data transmission
Economic development
Electromagnetics
Flight safety
Frequency estimation
Frequency hopping
hopping signal detection
Interception
Jamming
Monitoring
Noise threshold
Parameter estimation
Signal detection
Signal monitoring
Signal processing
Time-frequency analysis
Unmanned aerial vehicles
Vector quantization
Waveforms
ISSN:2169-3536, 2169-3536
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Shrnutí: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 ++ , DBSCAN, Multi-hop and Auto-correlation methods. The results show that the method has certain robustness and has a good application prospect.
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content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3070491