基于KF-LSTM组合模型的短期电离层TEC预测

Short-Term Ionospheric TEC Prediction Based on KF-LSTM Combination Model

针对电离层总电子含量(TEC)时间序列非线性、非平稳性等特点,提出以卡尔曼滤波对电离层TEC数据进行预处理为基础,融合长短期记忆神经网络模型,构建KF-LSTM短期电离层组合模型预测TEC的方法,并利用该模型预测2016年和2018年4个时段的高、中、低纬度及赤道地区36个格网点提前1 d的电离层TEC。结果表明,KF-LSTM组合模型预测效果优于传统BP神经网络模型和单纯的LSTM模型。在赤道地区,其预测效果与C1PG模型相当;在15°~75°N地区,其预测效果优于C1PG模型。

In view of the nonlinear and non-stationary characteristics of the ionospheric total electron content(TEC),this paper proposes a short-term ionospheric TEC forecast model,KF-LSTM,based on the long short-term memory(LSTM)neural network.In data processing,Kalman filtering is introduced to preprocess the ionospheric TEC data of the Center for Orbit Determination in Europe(CODE).Meanwhile,the model is used to predict regional ionospheric TEC at 36 grid points in the global high,middle,low latitudes and equatorial regions in 2016 and 2018.The results indicate that the KF-LSTM prediction performance is superior to traditional BP neural network models and LSTM models in different latitude regions.In the equatorial region,its predictive performance is comparable to the C1PG model;in the 15°N-75°N region,the prediction effect is better than the C1PG model.