基于深度学习的卫星导航信号伪距偏差联合估计方法OA

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.