Stable and reliable high-precision satellite orbit products are the prerequisites for the positioning services with high performance.In general,the positioning accuracy depends strongly on the quality of satellite orb...Stable and reliable high-precision satellite orbit products are the prerequisites for the positioning services with high performance.In general,the positioning accuracy depends strongly on the quality of satellite orbit and clock products,especially for absolute positioning modes,such as Precise Point Positioning(PPP).With the development of real-time services,real-time Precise Orbit Determination(POD)is indispensable and mainly includes two methods:the ultra-rapid orbit prediction and the real-time filtering orbit determination.The real-time filtering method has a great potential to obtain more stable and reliable products than the ultra-rapid orbit prediction method and thus has attracted increasing attention in commercial companies and research institutes.However,several key issues should be resolved,including the refinement of satellite dynamic stochastic models,adaptive filtering for irregular satellite motions,rapid convergence,and real-time Ambiguity Resolution(AR).This paper reviews and summarizes the current research progress in real-time filtering POD with a focus on the aforementioned issues.In addition,the real-time filtering orbit determination software developed by our group is introduced,and some of the latest results are evaluated.The Three-Dimensional(3D)real-time orbit accuracy of GPS and Galileo satellites is better than 5 cm with AR.In terms of the convergence time and accuracy of kinematic PPP AR,the better performance of the filter orbit products is validated compared to the ultra-rapid orbit products.展开更多
An accurate mathematical representation of soil particle-size distribution(PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, man...An accurate mathematical representation of soil particle-size distribution(PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, many databases do not contain full PSD data,but instead contain only the clay, silt, and sand mass fractions. The objective of this study was to evaluate the abilities of four PSD models(the Skaggs model, the Fooladmand model, the modified Gray model GM(1,1), and the Fredlund model) to predict detailed PSD using limited soil textural data and to determine the effects of soil texture on the performance of the individual PSD model.The mean absolute error(MAE) and root mean square error(RMSE) were used to measure the goodness-of-fit of the models, and the Akaike's information criterion(AIC) was used to compare the quality of model fits. The performance of all PSD models except the GM(1,1) improved with increasing clay content in soils. This result showed that the GM(1,1) was less dependent on soil texture.The Fredlund model was the best for describing the PSDs of all soil textures except in the sand textural class. However, the GM(1,1) showed better performance as the sand content increased. These results indicated that the Fredlund model showed the best performance and the least values of all evaluation criteria, and can be used using limited soil textural data for detailed PSD.展开更多
基金National Natural Science Foundation of China(Grand No.41904021).
文摘Stable and reliable high-precision satellite orbit products are the prerequisites for the positioning services with high performance.In general,the positioning accuracy depends strongly on the quality of satellite orbit and clock products,especially for absolute positioning modes,such as Precise Point Positioning(PPP).With the development of real-time services,real-time Precise Orbit Determination(POD)is indispensable and mainly includes two methods:the ultra-rapid orbit prediction and the real-time filtering orbit determination.The real-time filtering method has a great potential to obtain more stable and reliable products than the ultra-rapid orbit prediction method and thus has attracted increasing attention in commercial companies and research institutes.However,several key issues should be resolved,including the refinement of satellite dynamic stochastic models,adaptive filtering for irregular satellite motions,rapid convergence,and real-time Ambiguity Resolution(AR).This paper reviews and summarizes the current research progress in real-time filtering POD with a focus on the aforementioned issues.In addition,the real-time filtering orbit determination software developed by our group is introduced,and some of the latest results are evaluated.The Three-Dimensional(3D)real-time orbit accuracy of GPS and Galileo satellites is better than 5 cm with AR.In terms of the convergence time and accuracy of kinematic PPP AR,the better performance of the filter orbit products is validated compared to the ultra-rapid orbit products.
基金supported by the Rice Research Institute, Rasht of Iran
文摘An accurate mathematical representation of soil particle-size distribution(PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, many databases do not contain full PSD data,but instead contain only the clay, silt, and sand mass fractions. The objective of this study was to evaluate the abilities of four PSD models(the Skaggs model, the Fooladmand model, the modified Gray model GM(1,1), and the Fredlund model) to predict detailed PSD using limited soil textural data and to determine the effects of soil texture on the performance of the individual PSD model.The mean absolute error(MAE) and root mean square error(RMSE) were used to measure the goodness-of-fit of the models, and the Akaike's information criterion(AIC) was used to compare the quality of model fits. The performance of all PSD models except the GM(1,1) improved with increasing clay content in soils. This result showed that the GM(1,1) was less dependent on soil texture.The Fredlund model was the best for describing the PSDs of all soil textures except in the sand textural class. However, the GM(1,1) showed better performance as the sand content increased. These results indicated that the Fredlund model showed the best performance and the least values of all evaluation criteria, and can be used using limited soil textural data for detailed PSD.