摘要
差分码偏差(DCB)的存在会影响电离层总电子含量(TEC)计算的精度和伪距观测精度,从而影响导航定位授时和气象研究的结果。为了准确估算DCB,分析了2021—2022年中国科学院给出的全球定位系统(GPS)频间DCB数据的时序变化,提出一种LR-KF-LSTM组合模型对DCB进行准确预测并对预测结果进行分析。实验结果表明,该方法的预测结果的平均绝对百分比误差小于1.9%,平均绝对误差小于0.03 ns,均方根误差小于0.04 ns。与LSTM模型、BP神经网络模型及中国科学院(CAS)产品DCB值的对比显示,在不同太阳活动状态和不同磁场状态下,该组合模型预测性能均较优。该组合网络模型可以对卫星DCB进行有效预测,还能为解决CAS产品DCB数据存在的单日或多日缺失问题提供参考。
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 temporal 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 activity 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.
作者
廖思明
尚俊娜
苏明坤
LIAO Siming;SHANG Junna;SU Mingkun(School of Communication Engineering,Hangzhou Dianzi University,1158 Second-Baiyang Street,Hangzhou 310018,China)
出处
《大地测量与地球动力学》
北大核心
2025年第9期915-921,共7页
Journal of Geodesy and Geodynamics
基金
浙江省教育厅科研项目(Y202455360)。
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
differential code bias(DCB)
long short-term memory(LSTM)neural network
Kalman filter(KF)
linear regression(LR)
navigation positioning
