BDS卫星轨道误差预测模型

BDS Broadcast Ephemeris Orbit Error Prediction Model

卫星轨道的准确性和稳定性是北斗卫星导航系统(BDS)的核心之一,也是影响卫星导航系统服务性能的关键部分。针对BDS卫星轨道误差中存在的非线性现象,提出了以BP神经网络算法为基础的BDS卫星轨道误差预测模型。通过某一段时间解算的卫星位置与速度,结合时间与具有周期性摄动的改正数作为训练样本进行训练,并利用训练好的神经网络模型对当前时刻的轨道误差进行预测。结果表明,该模型能够较好地拟合和卫星轨道中的非线性误差,运用该模型对卫星轨道的位置和速度误差进行预测补偿,能够提升卫星轨道的预报精度,降低系统性误差。

The accuracy and stability of the satellite orbit is one of the core parts of the entire navigation system and a key factor affecting the performance of the navigation system.Through the analysis of satellite orbit error,there is a nonlinear phenomenon in satellite orbit error.For this error information that cannot be represented by mathematical model,the satellite orbit error prediction model of BP neural network is established.The satellite position and velocity solved by a certain period of time,combined with the time and the periodic perturbation correction number are used as training samples for training,and the trained neural network model is used to predict the current orbit error.The results show that the model can fit well with the nonlinear error in the satellite orbit.The model can predict and compensate the position and velocity error of the satellite orbit,which can improve the prediction accuracy of the satellite orbit and reduce the systematic error.