全球定位系统2026，Vol.51Issue(1)：58-71,14.DOI:10.12265/j.gnss.2025214
基于深度学习的卫星导航信号伪距偏差联合估计方法
Joint estimation of pseudorange bias in satellite navigation signals based on deep learning
摘要
Abstract
Pseudorange bias refers to the constant bias in pseudorange measurements produced by receivers of different technical states due to the non-ideal characteristics of satellite navigation signals,and it has become one of the major error sources limiting high-precision applications of GNSS.This study investigates the pseudorange bias of the B3I signal of the BeiDou Navigation Satellite System(BDS).First,the basic principle of the collocated-receiver double-difference method is presented,and the calculation of pseudorange bias is described.To address the difficulty of estimating pseudorange bias in medium-baseline and long-baseline scenarios using conventional methods,a joint error estimation approach based on a hybrid convolutional neural network(CNN)and long short-term memory(LSTM)deep learning model is proposed.This method predicts and estimates the pseudorange double-difference residual components,including pseudorange bias.Real BDS observations from the WUH2_JFNG medium-and long-baseline with an inter-station distance of 12.9 km are used to construct the training,validation,and test datasets.The proposed model is comprehensively compared with a CNN and recurrent neural network(RNN)model in terms of prediction performance.Experimental results demonstrate that the proposed CNN-LSTM model achieves higher accuracy and better stability in the prediction task.Compared with the CNN-RNN model,the root mean square error(RMSE)value and mean absolute error(MAE)value are reduced by 10.83%and 11.10%respectively,while the R2 value is improved by 0.015 9.In addition,the proportions of prediction errors within±0.2 m and±0.5 m are increased by 2.77 and 3.78 percentage points respectively.The proposed model provides effective technical support for subsequent joint compensation of pseudorange bias and the improvement of positioning accuracy.关键词
卫星导航信号/非理想特性/伪距偏差/深度学习/CNN-LSTM模型Key words
satellite navigation signals/non-ideal characteristics/pseudorange bias/deep learning/CNN-LSTM model分类
信息技术与安全科学引用本文复制引用
杜尚真,贺成艳,张兆林,孙延栋,王伶..基于深度学习的卫星导航信号伪距偏差联合估计方法[J].全球定位系统,2026,51(1):58-71,14.基金项目
国家自然科学基金(12273046,U2541217) （12273046,U2541217）
