A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods...A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.展开更多
采用GNSS数据中心提供GPS/BDS钟差数据作为实验数据,对二次多项式模型(the Quadratic PolynoMial(QP)model)、灰色模型(the Gray system Model(GM(1,1)))、时间序列模型(the ARIMA time series model)、卡尔曼滤波模型(KF)、小波神经网...采用GNSS数据中心提供GPS/BDS钟差数据作为实验数据,对二次多项式模型(the Quadratic PolynoMial(QP)model)、灰色模型(the Gray system Model(GM(1,1)))、时间序列模型(the ARIMA time series model)、卡尔曼滤波模型(KF)、小波神经网络模型(Wavelet Neural Network(WNN))五种模型钟差短期预报的效果进行分析和比较,总结了各模型预报钟差的优点与不足。展开更多
The Fractional Cycle Bias(FCB)product is crucial for the Ambiguity Resolution(AR)in Precise Point Positioning(PPP).Different from the traditional method using the ionospheric-free ambiguity which is formed by the Wide...The Fractional Cycle Bias(FCB)product is crucial for the Ambiguity Resolution(AR)in Precise Point Positioning(PPP).Different from the traditional method using the ionospheric-free ambiguity which is formed by the Wide Lane(WL)and Narrow Lane(NL)combinations,the uncombined PPP model is flexible and effective to generate the FCB prod-ucts.This study presents the FCB estimation method based on the multi-Global Navigation Satellite System(GNSS)precise satellite orbit and clock corrections from the international GNSS Monitoring and Assessment System(iGMAS)observations using the uncombined PPP model.The dual-frequency raw ambiguities are combined by the integer coefficients(4,−3)and(1,−1)to directly estimate the FCBs.The details of FCB estimation are described with the Global Positioning System(GPS),BeiDou-2 Navigation Satellite System(BDS-2)and Galileo Navigation Satellite System(Galileo).For the estimated FCBs,the Root Mean Squares(RMSs)of the posterior residuals are smaller than 0.1 cycles,which indicates a high consistency for the float ambiguities.The stability of the WL FCBs series is better than 0.02 cycles for the three GNSS systems,while the STandard Deviation(STD)of the NL FCBs for BDS-2 is larger than 0.139 cycles.The combined FCBs have better stability than the raw series.With the multi-GNSS FCB products,the PPP AR for GPS/BDS-2/Galileo is demonstrated using the raw observations.For hourly static positioning results,the performance of the PPP AR with the three-system observations is improved by 42.6%,but only 13.1%for kinematic positioning results.The results indicate that precise and reliable positioning can be achieved with the PPP AR of GPS/BDS-2/Galileo,supported by multi-GNSS satellite orbit,clock,and FCB products based on iGMAS.展开更多
基金2022 Basic Scientific Research Project supported by Liaoning Provincial Education Department(No.LJKMZ20221686)。
文摘A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.
文摘采用GNSS数据中心提供GPS/BDS钟差数据作为实验数据,对二次多项式模型(the Quadratic PolynoMial(QP)model)、灰色模型(the Gray system Model(GM(1,1)))、时间序列模型(the ARIMA time series model)、卡尔曼滤波模型(KF)、小波神经网络模型(Wavelet Neural Network(WNN))五种模型钟差短期预报的效果进行分析和比较,总结了各模型预报钟差的优点与不足。
基金The National Key Research and Development Program of China(2018YFC1505102)the Programs of the National Natural Science Foundation of China(41774025,41731066)+2 种基金the Special Fund for Technological Innovation Guidance of Shaanxi Province(2018XNCGG05)the Special Fund for Basic Scientific Research of Central Colleges(CHD300102269305,CHD300102268305)the Grand Projects of the BDS-2 System(GFZX0301040308)supported this study.
文摘The Fractional Cycle Bias(FCB)product is crucial for the Ambiguity Resolution(AR)in Precise Point Positioning(PPP).Different from the traditional method using the ionospheric-free ambiguity which is formed by the Wide Lane(WL)and Narrow Lane(NL)combinations,the uncombined PPP model is flexible and effective to generate the FCB prod-ucts.This study presents the FCB estimation method based on the multi-Global Navigation Satellite System(GNSS)precise satellite orbit and clock corrections from the international GNSS Monitoring and Assessment System(iGMAS)observations using the uncombined PPP model.The dual-frequency raw ambiguities are combined by the integer coefficients(4,−3)and(1,−1)to directly estimate the FCBs.The details of FCB estimation are described with the Global Positioning System(GPS),BeiDou-2 Navigation Satellite System(BDS-2)and Galileo Navigation Satellite System(Galileo).For the estimated FCBs,the Root Mean Squares(RMSs)of the posterior residuals are smaller than 0.1 cycles,which indicates a high consistency for the float ambiguities.The stability of the WL FCBs series is better than 0.02 cycles for the three GNSS systems,while the STandard Deviation(STD)of the NL FCBs for BDS-2 is larger than 0.139 cycles.The combined FCBs have better stability than the raw series.With the multi-GNSS FCB products,the PPP AR for GPS/BDS-2/Galileo is demonstrated using the raw observations.For hourly static positioning results,the performance of the PPP AR with the three-system observations is improved by 42.6%,but only 13.1%for kinematic positioning results.The results indicate that precise and reliable positioning can be achieved with the PPP AR of GPS/BDS-2/Galileo,supported by multi-GNSS satellite orbit,clock,and FCB products based on iGMAS.
