Short-Term Ionospheric TEC Prediction Based on KF-LSTM Combination Model
Abstract 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.
Key words :
ionospheric total electron content(TEC)
Kalman filter
long- and short-term memory(LSTM)
short-term prediction
Cite this article:
LI Lei,LI Jing,YANG Chen. Short-Term Ionospheric TEC Prediction Based on KF-LSTM Combination Model[J]. jgg, 2023, 43(10): 1020-1025.
LI Lei,LI Jing,YANG Chen. Short-Term Ionospheric TEC Prediction Based on KF-LSTM Combination Model[J]. jgg, 2023, 43(10): 1020-1025.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2023/V43/I10/1020
[1]
LIU Xiangjie, LIU Xiaosheng, ZHANG Longwei. Dam Deformation Prediction Based on VMD-HPO-BiLSTM [J]. jgg, 2023, 43(8): 851-855.
[2]
CHEN Xi, GAO Yaping. Coseismic Displacement Obtained by Combining High-Rate GNSS and Strong Seismograph Observation [J]. jgg, 2023, 43(7): 674-678.
[3]
GE Zhimin, JIANG Jinguang, ZHANG Chao, WU Jiaji, LIU Guonian. Application of Improved Robust and Adaptive EKF Algorithm in GNSS/INS Integrated Navigation [J]. jgg, 2023, 43(7): 740-744.
[4]
YUAN Xitun, WEN Yongxiao, CHEN Xinyu. Prediction Method and Applicability of Mining Area Surface Subsidence Based on Multi-Model Fusion [J]. jgg, 2023, 43(3): 232-238.
[5]
ZHANG Can, Lü Weicai, GUO Zhongchen, LIU Yu, XIE Shicheng. An Optimized Combined Prediction Model for Surface Subsidence Based on GA-KF and BP-Adaboost [J]. jgg, 2023, 43(2): 203-208.
[6]
WU Han,HUANG Ling,LIU Lilong,HUANG Liangke,ZHANG Hongping. Short Term Prediction Model of Ionospheric TEC Based on SSA-LSTM [J]. jgg, 2022, 42(6): 626-630.
[7]
GONG Yun,XIN Jie,NAN Shoujin. A Denoising Model of MEMS Gyroscope with Hurst Exponent [J]. jgg, 2022, 42(5): 457-461.
[8]
LIN Xueyuan,WANG Ping,XU Jialong,LIU Lining,CHEN Xiangguang. An Optimal Fusion Algorithm for GNSS/CNS/SINS Integrated Navigation Based on Sequential UKF [J]. jgg, 2022, 42(12): 1211-1215.
[9]
WANG Fu,HAN Baomin,HU Liangliang,MENG Hao,GUO Zhenhua. Research on GNSS/INS Integrated Navigation Algorithm in Complex Urban Environments [J]. jgg, 2022, 42(1): 15-20.
[10]
TANG Jun,LI Yinjian,ZHONG Zhengyu,GAO Xin. Prediction Model of Ionospheric TEC by EOF and LSTM Neural Network [J]. jgg, 2021, 41(9): 911-915.
[11]
HUANG Jiawei,LU Tieding,HE Xiaoxing,LI Wei. Short Term Prediction Model of Ionospheric TEC Based on Residual Correction of Prophet-Elman [J]. jgg, 2021, 41(8): 783-788.
[12]
LIU Lili,LIN Xueyuan,YU Feng,CHEN Xiangguang. A Filtering Method of SINS/CNS/GNSS Integrated Navigation System [J]. jgg, 2021, 41(7): 676-681.
[13]
LU Tieding,HUANG Jiawei,HE Xiaoxing,Lü Kaiyun. Short-Term Ionospheric TEC Prediction Using EWT-Elman Combination Model [J]. jgg, 2021, 41(7): 666-671.
[14]
MAO Yue,ZHU Yongxing,SONG Xiaoyong. Accuracy Analysis of Broadcast Ionosphere Model of Global Navigation Satellite System
[J]. jgg, 2020, 40(9): 888-891.
[15]
CHANG Ming,YIN Haiquan. The Analysis of Vertical Deformation Characteristics in Shanxi-Hebei-Inner Mongolia Area Using Precise Leveling Data [J]. jgg, 2020, 40(10): 1079-1083.