A UAV Detection Algorithm Based on an Artificial Neural Network

被引：56
|
作者：
Zhang, Hao [1 ,2 ]
Cao, Conghui [1 ]
Xu, Lingwei [3 ]
Gulliver, T. Aaron [2 ]
机构：
[1] Ocean Univ China, Dept Elect Engn, Qingdao 266100, Peoples R China
[2] Univ Victoria, Dept Elect & Comp Engn, Victoria, BC V8W 2Y2, Canada
[3] Qingdao Univ Sci & Technol, Dept Informat Sci & Technol, Qingdao 266061, Peoples R China
来源：
IEEE ACCESS | 2018年 / 6卷
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Unmanned aerial vehicle (UAV); wireless sensor networks; artificial neural network (ANN); signal detection; slope; kurtosis; skewness; CLASSIFICATION; PERFORMANCE; FAULT; TIME;
D O I：
10.1109/ACCESS.2018.2831911
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an artificial neural network (ANN)-based detection algorithm for an unmanned aerial vehicle (UAV). The slope, kurtosis, and skewness of the signal received from the UAV are employed in this algorithm. The training of the three corresponding feature matrices is done using UAV, and non-UAV signals can be classified effectively for the UAV sensor network based on ANN. Outdoor data over a bridge in the Jimo District, Qingdao, and indoor data from a research laboratory are used for system training and evaluation. The results obtained show that the proposed detection algorithm based on an ANN outperforms methods based on the slope, kurtosis, and skewness of the received signal in terms of the error rate. The recognition rate with the proposed algorithm is greater than 82% within a distance of 3 km, which is better than other UAV detection methods such as active radar, acoustic, and visual recognition.
引用
收藏
页码：24720 / 24728
页数：9
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