Prediction of Lunar Libration Parameters Using LSTM

WEI Erhu; REN Xiaobin; LIU Jingnan; LI Lianyan; WU Shuguang; NIE Guigen

doi:10.13203/j.whugis20200318

Volume 42 Issue 11

Nov. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(11): 1815-1822. > DOI: 10.13203/j.whugis20200318

WEI Erhu, REN Xiaobin, LIU Jingnan, LI Lianyan, WU Shuguang, NIE Guigen. Prediction of Lunar Libration Parameters Using LSTM[J]. Geomatics and Information Science of Wuhan University, 2022, 47(11): 1815-1822. DOI: 10.13203/j.whugis20200318

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Prediction of Lunar Libration Parameters Using LSTM

1.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2.
GNSS Research Center, Wuhan University, Wuhan 430079, China
3.
School of Civil Engineering, Wuhan University, Wuhan 430072, China

Funds:

The National Key Research and Development Program of China 2018YFC1503600

the National Natural Science Foundation of China 41874036

More Information

Author Bio:
WEI Erhu, PhD, professor, specializes in space geodesy and deep space navigation. E-mail: ehwei@sgg.whu.edu.cn
Corresponding author:
REN Xiaobin, master. E-mail: xiaobinren@whu.edu.cn
Received Date: July 08, 2021
Available Online: November 15, 2022
Published Date: November 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives The prediction of lunar physical liberation is very important for precise positioning of lander and rover. The coordinates and physical libration of Chang'E-3 lander are calculated based on the very long baseline interferometry(VLBI) single point positioning model. Meanwhile, the parameters of the lunar libration are obtained in the 7-day arc.
Methods Based on the recursive neural network(RNN), a long-short term memory(LSTM) neural network is used to establish a prediction model of the lunar libration parameters. Using the coordinates of the lunar lander and the VLBI observation as input, selecting three Euler angles as output, 2 424 samples are used for training, 519 samples are used for testing, and 519 samples are used for verification.
Results The results show that the data of the verification set has a gradient of about 1 000 training and 9 iterations, and the maximum absolute error after training is only 6.2×10^-5(″)/s. The experimental results show that the three Euler angles Ω、i、μ of LSTM are accurate to 97.8%, 99.7%, 97.2%, and the three Euler angles of RNN networks are accurate to 95.2%, 98.5%, 95.8%.
Conclusions It is proved that the LSTM network has higher prediction accuracy than the RNN network. Compared with the predicted results of JPL DE421 ephemeris, it is proved that the LSTM network has the higher prediction accuracy.

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