Research On GPS Precise Point Positioning With Adaptive Kalman Filter

Precise Point Positioning(PPP)is a high-precision absolute positioning method which is using precise orbit and clock products provided by IGS and dual-frequency pseudorange and carrier phase measurements in single station.There are some difficulties and limitations in the processing of PPP.First,the method of cycle slip detection is limited by the types of observation.The data preprocessing is more difficult,too.Second,we need to consider a number of measurement errors and then eliminate or weaken them.Third,the integer ambiguity is difficult to fix,this leads to the slower convergence speed and the positioning accuracy is also affected.Fourth,the real-time precision orbit and clock products' acquisition and transmission was difficult.There is a limitation in the process of Kalman filter.In the process of kinematic PPP,the noise of the motion model may not be able to simulate accurately and the measurement noise maybe not normally distributed,so the accuracy of the positioning results may be affected.In this study,we use PPP classical model.Reducing and eliminating the influence of error terms in the PPP by mathematical modeling.Considering the influence of cycle slip,clock jump and difference code bias in the data preprocessing.Lagrange interpolation was used to get the precise orbit and clock data at any time.Adaptive Robust Kalman Filter and modified Innovation-Based Adaptive Estimation are used in the kinematic PPP.This study compared these two adaptive Kalman filters with standard Kalman filter and analyzed whether they can stabilize the influence that abnormal observation applied to the positioning result.