| Precise Point Position(PPP)is a high-precision positioning method based on state space domain correction information.PPP technology uses a single receiver to receive satellite signals,which can improve the distance between stations without considering the relative positioning.The flexibility of GNSS for precise positioning.At present,PPP technology has been widely used in water vapor prediction,precision timing long-distance dynamic positioning and other related fields.However,in harsh environments such as urban buildings,mountainous areas and open-pit mines,single-system PPP cannot achieve high-precision positioning because of the small number of visible satellites and poor satellite geometry.With the development of satellite navigation systems in various countries,the number of satellites is gradually increasing.Increased,multi-system satellite joint positioning can well solve the problem of insufficient satellite number and poor geometric structure in the harsh observation environment.In view of this,this paper studies the precise single point positioning model from the perspective of multi-system GNSS joint.The research content of this paper is mainly analyzed from the following aspects:(1)Unified expression of GNSS multi-system observations.On the basis of introducing the multi-system joint positioning theory,the unified expression equations of GNSS multi-system observations are derived by considering the spatial-temporal reference differences.Finally,the related error sources involved in GNSS navigation and positioning and its correction methods are given.(2)Construction of multi-system joint PPP positioning model.Based on the analysis of the common function model of precise single point positioning,the PPP function model of multi-system joint positioning is established based on the unified expression of GNSS multi-system observations.The multi-system joint is determined by the integrated elevation angle method and Helmert variance component estimation method.A stochastic model of PPP.At the same time,three methods of parameter estimation are introduced.The parameter estimation method used in this paper is Kalman filter.(3)Multi-system joint PPP convergence performance analysis.Using the IGS site data,the UofC function model and the UD function model were used to perform precise single-point positioning performance analysis on seven different combinations of the three systems.In the UofC model,the combined system convergence rate is higher than that of the single system.The convergence time of BDS is up to 65min,and the convergence time of the three-system GPS/GLONASS/BDS combination system is about 15min.In the UD model,the convergence rate of the combined system is better than that of the single system,and the BDS single system has the lowest convergence rate.The convergence time of the three-system GPS/GLONASS/BDS combination is about 30 minutes,and the single system BDS has the longest convergence time,and the time is about 60 minutes.(4)Multi-system joint PPP positioning accuracy analysis.In terms of positioning accuracy,the single-day solution positioning accuracy of the two functional models can reach sub-millimeter.In the UofC model,the accuracy of the combined system is higher than that of the single system.The optimal three-system combination has a single-day root mean square error horizontal direction of less than 1 cm and a zenith direction of about 2 cm.The worst single system BDS has a single-day root mean square error horizontal direction of about 2 cm and a zenith direction of about 5 cm.In the UD function model,the BDS system has relatively poor positioning accuracy in the three directions of N?E and U,with the horizontal direction being about 2 cm and the zenith direction being about 5 cm.The three-system GPS/GLONASS/BDS combination has the highest positioning accuracy,and the square root error of single-day solution is also within 1cm,and the root mean square of the zenith is about 2cm.The UofC model has better positioning performance than the UD type,mainly because the UofC model reduces the noise level of the combined observations relative to the UD model.Figure[27];Table[8];Reference[80]... |
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