基于ICEEMDAN和SSA-LSTM组合模型的电离层TEC预测  

Ionospheric TEC prediction based on ICEEMDAN and SSA-LSTM combined models

作　　者：张振国 孙希延[1,2,3,4] 纪元法 贾茜子[1,2] ZHANG Zhenguo;SUN Xiyan;JI Yuanfa;JIA Xizi(Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology,Guilin 541004,China;School of Information and Communicaiton,Guilin University of Electronic Technology,Guilin 541004,China;National&Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service,Guilin 541004,China;GUET-Nanning E-Tech Research Institute Co.,Ltd.,Nanning 530031,China)

机构地区：[1]桂林电子科技大学广西精密导航技术与应用重点实验室,桂林541004 [2]桂林电子科技大学信息与通信学院,桂林541004 [3]卫星导航定位与位置服务国家地方联合工程研究中心,桂林541004 [4]南宁桂电电子科技研究院有限公司,南宁530031

出　　处：《全球定位系统》2025年第1期48-59,共12页Gnss World of China

基　　金：广西科技厅项目(桂科AA23062038,桂科AD22080061,桂科AB23026120,桂科ZY22096026);国家自然科学基金(U23A20280,62161007,62061010);“认知无线电与信息处理”教育部重点实验室2023年主任基金项目(CRKL230104);广西高校中青年教师科研基础能力提升项目(2022KY0181);广西研究生教育创新计划项目(YCSW2024329)。

摘　　要：针对电离层总电子含量(total electron content,TEC)具有非线性和非平稳性的特性及单一长短期记忆神经网络(long short-term memory,LSTM)模型在预测中存在精度不高且易陷入局部最优等问题,在改进的自适应噪声完备集合经验模态分解(improved complete ensemble EMD with adaptive noise,ICEEMDAN)和样本熵(sample entropy,SE)算法的基础上,结合麻雀搜索算法(sparrow search algorithm,SSA)和LSTM构建电离层TEC组合预测模型,并对太阳活动低年平静期和太阳活动高年扰动期电离层TEC连续5 d的预测精度分析.实验结果表明,本文组合模型相较于单一LSTM模型和SSA-LSTM模型在低太阳活动平静期和高太阳活动扰动期的不同经纬度下,均方根误差(root mean square error,RMSE)分别最大降低1.06 TECU和2.25 TECU,平均绝对误差(mean absolute error,MAE)分别最大降低了0.74 TECU和1.68 TECU,平均相对精度分别最大提升了7.63%和8.97%,组合模型的预测效果要明显优于单一LSTM模型和SSA-LSTM模型.Aiming at the nonlinear and non-stationary characteristics of ionospheric total electron content(TEC)and the problems that a single LSTM model has in prediction,such as low accuracy and easy to fall into local optimality,On the basis of improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and sample entropy(SE)algorithms,combined with sparrow search algorithm(SSA)and long short-term memory(LSTM)neural network,a combined prediction model for TEC in the ionosphere was constructed,and analyzes the prediction accuracy of ionospheric TEC during the low year calm period of solar activity and the high year disturbance period of solar activity for 5 consecutive days.The experimental results show that compared with the single LSTM model and the SSA-LSTM model,the root mean square error of the combined model in this paper is reduced by 1.06 TECU and 2.25 TECU respectively under different latitude and longitude of the low solar activity quiet period and the high solar activity disturbance period.The average absolute error decreased by 0.74 TECU and 1.68 TECU respectively,and the average relative accuracy increased by 7.63%and 8.97%respectively.The prediction effect of the combined model was significantly better than that of the single LSTM model and the SSA-LSTM model.

关 键 词：电离层 总电子含量(TEC)预测 改进的自适应噪声完备集合经验模态分解(ICEEMDAN) 样本熵(SE) 麻雀搜索算法(SSA) 长短期记忆神经网络(LSTM) 

分 类 号：P228.4[天文地球—大地测量学与测量工程]