利用LSTM网络预测月球物理天平动参数

Prediction of Lunar Libration Parameters Using LSTM

    摘要
  • 摘要: 利用中国探月甚长基线干涉测量（very long baseline interferometry, VLBI）观测数据改进月球物理天平动参数的预测精度，对于着陆器和巡视器的精密定位具有重要意义。利用VLBI单点定位模型解算得到“嫦娥三号”（Chang’E-3，CE-3）着陆器的坐标和物理天平动，分别采用循环神经网络（recursive neural network, RNN）和长短期记忆（long-short term memory, LSTM）网络进行物理天平动的预测。选取月球着陆器的坐标和VLBI观测量作为输入量，将3个欧拉角Ω, i, μ作为输出量，将11 323个样本用于训练，2 315个样本用于测试，2 315个样本用于验证，1 000个样本用作与预测结果进行对比。结果显示, 验证集的数据经过1 000次训练和9次迭代训练后的梯度约为6.2×10-5(″)/s，证明了LSTM网络与RNN的可靠性。LSTM网络和RNN的3个欧拉角的预测精度分别达到了97.8%、99.7%、97.2%和95.2%、98.5%、95.8%，LSTM网络的预测精度更高。与DE421星历对欧拉角的预测结果进行比较，结果证明了LSTM网络预测精度更高。

     

    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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