大地测量与地球动力学2025，Vol.45Issue(9)：915-921,7.DOI:10.14075/j.jgg.2024.10.476
基于LR-KF-LSTM模型的差分码偏差预测分析
Prediction of Differential Code Bias Based on LR-KF-LSTM Model
摘要
Abstract
The presence of differential code bias(DCB)can affect the accuracy of total electron content(TEC)calculations and pseudorange observations,thereby impacting navigation,positioning,timing,and meteorological research results.In order to accurately estimate DCB,this paper analyzes the tem-poral variations of GPS inter-frequency DCB data provided by the Chinese Academy of Sciences(CAS)from 2021 to 2022 and proposes an LR-KF-LSTM combined model for precise prediction and analysis of DCB.Experimental results indicate that the average absolute percentage error of this method is less than 1.9％,the average absolute error is less than 0.03 ns,and the root mean square error is less than 0.04 ns.Compared with the LSTM model,BP neural network model,and the DCB values from the CAS product,the combined model shows better prediction performance under different solar activ-ity and geomagnetic conditions.This combined network model can effectively predict satellite DCB and also provides a reference for addressing the issue of single-day or multi-day missing data in the DCB data CAS products.关键词
差分码偏差/长短期记忆神经网络/卡尔曼滤波/线性回归/导航定位Key words
differential code bias(DCB)/long short-term memory(LSTM)neural network/Kalman filter(KF)/linear regression(LR)/navigation positioning分类
天文与地球科学引用本文复制引用
廖思明,尚俊娜,苏明坤..基于LR-KF-LSTM模型的差分码偏差预测分析[J].大地测量与地球动力学,2025,45(9):915-921,7.基金项目
浙江省教育厅科研项目(Y202455360). Scientific Research Project of the Education Department of Zhejiang Province,No.Y202455360. （Y202455360）
