【作者】 周建华； 杨龙； 徐波； 谢廷峰；

【Author】 Zhou Jianhua~1,Yang Long~1,Xu Bo~2,Xie Tingfeng~1 1.Beijing Global Information Application and Exploitation Center,Beijing,China 2.Nanjing University of Aeronautics and Astronautics,Nanjing,China

【机构】 北京环球信息应用开发中心； 南京航空航天大学；

【摘要】 基于神经网络混合建模的思想提出了一种针对导航卫星的中长期轨道预报方法,在原动力学模型的基础上引入神经网络模型作为补偿,从而获得了新的预报模型。在训练过程中神经网络通过学习动力学模型轨道预报误差来掌握其变化规律,并在预报过程中为动力学模型预报提供补偿,以提高预报精度。文中对GPS卫星动力学模型中长期预报误差的特点进行分析,然后根据所得结论提出了混合模型的中长期(15天以上)预报方案,最后通过对GPS卫星的仿真实验证明了混合模型的改进效果,结果表明新方法在15-40天的预报上表现出很好的改进效果。更多还原

【Abstract】 In this paper,a hybrid predicion modelling method based on neural network is introduced for navigation satellite.The new model is based on dynamical model and the neural network model is adopted to modify it.During the training phase,neural network model tries to approach the difference of dynamical model prediction product,then compensate it in the prediction phase.In this paper,the characteristics of DMM prediction error for GPS are explored,and then predition strategy for long term(longer than 15days) is designed,at the last,a group of tests on GPS satellites are processed to prove and evaluate the performance of the new method,the test results show that the improvement for 15-40days prediction is obvious.更多还原