基于补偿波形调整的导航卫星轨道预报方法

针对利用动力学模型得到的预报轨道随时间推移精度衰减较快的问题，尝试采用神经网络作为建模工具改进北斗导航卫星轨道预报精度。对影响神经网络模型补偿效果的因素进行了详细分析，基于神经网络补偿波形调整策略制定了适应导航卫星短期、中期和长期预报的神经网络优化模型。利用实测数据进行了试验分析，结果表明，该方法可以显著改进利用动力学模型得到的预报轨道精度。短期预报中，当采用的训练样本距离当前时刻大于10 d时，应移动补偿波形；中长期预报中均应移动补偿波形。相比补偿波形不调整的神经网络模型，采用基于补偿波形调整的神经网络优化模型后，预报弧长为8、15、30 d时，改进率分别提高了2.3%、6.7%、10%。

A Method of Improving Orbit Prediction for Navigation Satellite Based on Adjusting Compensable Wave

A new method of satellite orbit prediction based on artificial neural network (ANN) model is proposed in order to improve the orbital precision for BeiDou satellites. Several factors influencing compensation effect were analyzed particularly. Short-term, middle-term and long-term forecasting project were established based on adjusting compensable wave. The experiment results showed that, the orbit prediction errors based on ANN model were less than those based on dynamical model. In short-term orbit prediction, compensable wave needed to be moved when the time between training sample and current time were greater than 10 d, and compensable wave needed to be moved in middle-term and long-term orbit prediction. Comparison with that of un-adjusting compensable wave, rates of improvement were respectively improved 2.3%, 6.7%, 10% based on adjusting compensable wave when the prediction arcs are 8, 15, 30 d.