基于LSTM神经网络的短期轨道预报

doi:10.12305/j.issn.1001-506X.2022.03.26

Abstract

Abstract:

Aiming at the problems of high modeling cost and lack of target space environment information in satellite autonomous orbit prediction and a large number of non cooperative target orbit prediction based on dynamic model, a neural network orbit prediction method driven by error data is proposed. Based on the analytical dynamic model, this method uses the long short-term memory neural network to learn the error of historical orbit prediction and predict the prediction error of future short-term dynamic model, so as to correct the prediction results. The ajisai satellite orbit data and SGP4 (simplified general pertubations) dynamic model are selected to verify the effectiveness and performance of the proposed model. The experimental results show that the one-day prediction errors of the three axes in the geocentric inertial coordinate system are reduced to 16.87%, 17.66% and 19.58% respectively, which significantly improves the orbit prediction accuracy.

Key words: satellite orbit forecast, machine learning, long short-term memory (LSTM) neural network, time series analysis

CLC Number:

V44
P228

Xinyu ZHANG, Yuan LIU, Jianing SONG. Short-term orbit prediction based on LSTM neural network[J]. Systems Engineering and Electronics, 2022, 44(3): 939-947.

Figures/Tables 12

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References 30

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NORAD ID	轨道周期/min	轨道倾角/(°)	轨道偏心率	近地点km
16 908	116	50.0	0.001	1 485.9

卫星形状	质量/kg	卫星直径/m	有无动力	远地点/km
球形	685	2.15	无	1 503.7

输入序列步长	输入向量大小	网络层数	神经元个数	预测时长/min	X轴Pml(x)/%	Y轴Pml(y)/%	Z轴Pml(z)/%
150	450	2	650	400	10.26	9.52	9.30
220	660	2	950	800	11.96	13.25	12.36
360	1 080	2	1 500	1 440	16.87	17.66	19.58

输入序列步长	Pml(x)/%	输入序列步长	Pml(x)/%
270	43.13	400	29.81
300	38.10	450	28.79
360	30.45	—	—

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Short-term orbit prediction based on LSTM neural network

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