用门控循环单元实时预测空战飞行轨迹

doi:10.3969/j.issn.1001-506X.2020.11.17

Abstract

Abstract:

To increase the accuracy of the aircraft flight trajectory prediction and ensure real-time perfor-mance of the trajectory prediction, a trajectory prediction method using gated recurrent unit (GRU) is proposed. Flight simulations are conducted under different conditions with different maneuvers and numerous trajectory samples are acquired. Neural networks with different number of layers and neurons are designed and trained by the acquired trajectory samples. The network structure with minimum error on the test set is selected. The re-lative error and prediction time cost of the GRU networks, recurrent neural networks and back propagation networks are compared. Transformation matrix of coordinate is introduced to make the trajectory prediction unaffected by course and coordinate system. The absolute error of the three methods in a frequently changing trajectory are compared. The results indicate that the average absolute error of the proposed method is 18 m in x-axis, 11 m in y-axis and 22 m in z-axis approximately, and the error of the proposed method is significantly less than the other two methods and the average prediction time cost of it is about 2.4 ms, which meet the real-time requirements.

Key words: trajectory prediction, gated recurrent unit (GRU), recurrent neural network, air combat situation, transformation matrix of coordinate

CLC Number:

V247.5

Hongpeng ZHANG, Changqiang HUANG, Yongbo XUAN, Shangqin TANG. Real-time prediction of air combat flight trajectory using GRU[J]. Systems Engineering and Electronics, 2020, 42(11): 2546-2552.

Figures/Tables 9

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采样量	范围	间隔
航迹倾角/(°)	(-50, 50)	10
初始滚转角/(°)	(-100, 100)	20
速度/(m/s)	(250, 340)	10
目标滚转角/(°)	(-180, 180)	5

每层神经元个数	层数
每层神经元个数	1	2	3
20	2.35	1.33	1.22
22	2.07	0.75	1.43
24	1.53	0.92	1.03
26	1.22	0.88	1.51
28	2.10	1.10	2.65
30	1.94	0.99	2.51

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Real-time prediction of air combat flight trajectory using GRU

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