基于航迹预测的无人机短时航迹偏离检测方法

doi:10.12305/j.issn.1001-506X.2024.08.17

Abstract

Abstract:

Safeguarding unmanned aerial vehicle (UAV) flight safety has become a key issue in promoting the innovative application and scale development of unmanned aviation. To solve the safety hazards caused by trajectory deviation during the operation of UAV in low-altitude structured airway networks, a abnormal trajectory detection method (ATDM) is proposed. Firstly, a trajectory data preprocessing and reconstruction model is established to construct the trajectory data containing multidimensional attributes such as position, velocity, heading and so on. Secondly, the UAV short-term trajectory prediction model is constructed by using the trajectory data with multi-dimensional attributes as input, and the bidirectional long and short-term memory network algorithm is used. Finally, based on the multidimensional local anomaly factor between historical and prediction trajectory points, the trajectory deviation detection is transformed into a density classification problem of the trajectory points, and the UAV trajectory deviation detection method is established to realize the dynamic monitoring of the trajectory deviation status in the short-time range. Results show that ATDM has better accuracy advantage and real-time performance in short prediction time range.

Key words: trajectory anomaly detection, trajectory prediction, trajectory deviation, unmanned aerial vehicle (UAV)

CLC Number:

Gang ZHONG, Jiangying ZHOU, Sen DU, Honghai ZHANG, Hao LIU. Short-time trajectory deviation detection method for UAV based on trajectory prediction[J]. Systems Engineering and Electronics, 2024, 46(8): 2696-2708.

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名称	参数
深度学习框架	Tensorflow2.2.0
激活函数	Sigmoid/Tanh
损失函数	MSE
优化器	Adam
网络层数	256/128/5
训练轮次	50

特征	航迹点
时间	5/9/2022 15:39:36
编号	2
经度/(°)	118.781 848 31
纬度/(°)	31.941 673 23
高度/m	27.423
速度/(m/s)	0.09
航向/(°)	15

真实\预测	正常航迹	异常航迹
正常航迹	TP	FN
异常航迹	FP	TN

参数	k取值
参数	3	5	7	9
精度	0.895	0.902	0.888	0.885
召回率	0.991	0.992	0.996	1
F1得分	0.941	0.945	0.939	0.938

参数	模型
参数	LSTM	SVM	iForest	ATDM
精度	0.892	0.872	0.901	0.902
召回率	0.871	0.843	0.884	0.992
F1得分	0.881	0.857	0.892	0.945
是否需要标注	是	是	否	否

Short-time trajectory deviation detection method for UAV based on trajectory prediction

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