基于最佳线性无偏预测的精密单点定位方法OACSTPCD

In satellite navigation and positioning,Kalman filter is a common method to process massive observation data quickly and calculate the position coordinates accurately.To solve the problem that the approximate values of some parameters need to be calculated as prior informa-tion in actual positioning,we introduced the best linear unbiased predictor theory to estimate the initial values of parameters without prior infor-mation and replace the unknown prior variance of parameters with the variance of the residual.In the static and dynamic situations,we carried out the undifferenced and uncombined precise point positioning experiments,and compared with traditional filtering methods.The results show that the precise point positioning algorithm based on the best linear unbiased predictor can estimate the initial mean and variance of parameters without prior information,and the positioning accuracy is consistent with that of Kalman filter.