Research On Precise Point Positioning Withadditional State Constraints

Precise point positioning(PPP)is a precise positioning technology,which can achieve mm-dm positioning accuracy with only one receiver.The main advantage of PPP is to eliminate most of the errors in the positioning process by using refined error correction model and precise correction products obtained from the global network,so as to achieve high-precision absolute positioning of a single station without the assistance of reference station.In the past decade,PPP has become a hot topic in geodetic field,and has been widely used in various applications of GNSS,such as building deformation monitoring,atmospheric monitoring and modeling,air triangulation,geological disaster monitoring,etc.Traditional PPP usually uses Kalman filtering(KF)to solve unknown parameters,but the performance of KF depends on accurate dynamic model and initial value.If the dynamic model is not accurate or the initial value is set incorrectly,the filtering performance will decline or even diverge.In the actual process of parameter estimation,there will be some known functional relationships among the state parameters to be evaluated.These prior functional relationships can be used as constraints to improve the stability of KF,so as to enhance the positioning performance of PPP.Therefore,the main work of this paper is to develop a PPP algorithm based on additional state constraints.The main work of this paper is as follows:First,based on GNSS positioning principle and data processing process,the basic theory of PPP is systematically summarized,including commonly used types of observation,positioning model,data preprocessing strategy,error correction method,parameter estimation method,etc.Secondly,several commonly used equality constrains state estimation algorithms are introduced.The principles of different algorithms are analyzed and the advantages and disadvantages of various methods are compared.The simulation results show that the algorithm can improve the performance of KF parameter estimation.Thirdly,the KF method with additional equality state constraints is combined with PPP algorithm.The construction methods of constraint equations in different constraint scenarios are given.The experimental results show that compared with the traditional PPP model,the PPP algorithm with additional state constraints can effectively improve the positioning accuracy of and shorten the convergence time.Fourth,based on the open source program and MATLAB platform,the state constrained PPP algorithm with additional constraints is written,and the graphical user interface of the algorithm is developed by using MATLAB software design tool.The user can perform the PPP through simple operation,which has certain scientific research and engineering value.