Multi-Indicator Comprehensive Assessment for Observation Stochastic Model of PPP

In PPP, the stochastic model of observation determines the availability and reliability of positioning accuracy, and the observations are usually weighted according to the angle of the GNSS observation, and the smaller the angle of the observation, the more the influence of atmospheric noise and multipath on the observation data increases, and the accuracy of the observations decreases. Based on this, we proposed multi-indicator comprehensive assessment based on grey correlation analysis for observation stochastic modeling of PPP. The position dilution of precision (PDOP), carrier-to-noise density ratio (C/N0) and pseudorange multipath indicators are selected to construct a multi-indicator matrix. Firstly, the indicators are normalized, and then the entropy weight of each assessment indicator is calculated to determine the indicator weight. Meanwhile, after selecting the optimal indicator set, the matrix is constructed to find the grey correlation coefficient and finally the grey correlation degree. According to the above method, the comprehensive assessment results of the quality of satellite observation data for each epoch can be obtained, and the PPP weight array can be established. One-week observations from 243 MGEX stations are selected to conduct GPS-only, Galileo-only and BDS-3-only kinematic PPP, the stochastic model using the highest-elevation and the proposed method is applied, respectively. The results show that, compared with the traditional method, the positioning accuracies and convergence time all can be improved using the proposed method. The positioning accuracies of GPS can be improved by about 4.23%, 8.66%, 5.04% and 5.46% in the east(E), north(N), up(U) and three-dimensional(3D) directions, respectively; 15.96%, 14.25%, 14.72% and 15.01% for Galileo; and 13.53%, 8.42%, 11.65% and 11.40% for BDS-3. The average improvements of convergence time in the east, north and up directions are 5.53%, 7.80% and 5.01% for GPS, BDS-3 and Galileo, respectively.