导航卫星钟差预报的Elman神经网络算法研究

针对卫星钟差不能被精确模型化的问题，将具有较强记忆功能和强大计算能力的Elman神经网络运用到卫星钟差预报中，提出适用于卫星钟差预报的Elman模型。首先对原始钟差数据进行一次差处理，然后选择合适的神经网络结构建立预报效果最佳的Elman钟差预报模型，最后选用国际GNSS服务（IGS）提供的精密钟差数据进行GPS卫星钟差预报，并与二次多项式模型、附加周期项的多项式模型和灰色系统模型进行对比分析。结果表明，Elman模型进行1 d、7 d和30 d钟差预报的精度得到显著提高，分别达到亚ns、ns和μs级，表明该模型的钟差预报性能优于3种常用模型，在卫星钟差预报中具有可行性。

Research on the Elman Neural Network Algorithm to Predict Navigation Satellite Clock Bias

Aiming at the problem that satellite clock bias cannot be accurately modeled,this paper applies the Elman neural network with strong memory function and powerful computing ability to satellite clock bias prediction and proposes an Elman model suitable for satellite clock bias prediction.First,we perform one-difference process on the original clock bias data.Then,we select the appropriate neural network structure to establish the Elman clock bias prediction model with the best forecast effect.Finally,precise clock bias data provided by the international GNSS service(IGS)is used to predict GPS satellite clock bias,and comparison analysis is performed with the quadratic polynomial model,the polynomial model with additional periodic terms,and the gray system model.The experimental results show that the accuracy of 1-day,7-days,and 30-days clock bias predictions of the Elman model have been significantly improved,reaching sub-nanosecond,nanosecond,and microsecond levels,respectively,proving that clock bias prediction performance of the model is superior to the three commonly used models and establishing the feasibility of the new model in satellite clock bias prediction.