Prediction of Lunar Libration Parameters Using LSTM

  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.