A Densification Method for Base Station Observation Data and its Application to Post Processing of Differential GNSS

When the sampling rate of base station is lower than that of the rover station, it's impossible to obtain the coordinates of the rover station at all epochs with a conventional differential GNSS post-processing method. To solve this problem, a method based on PPP model is proposed to construct the virtual observation data of the non-sampling points of base station. The method separates the receiver clock error and tropospheric zenith wet delay from the observation error, and estimates them simultaneously with ionosphere ambiguity, after which the distance between the satellite and the station is obtained by base station's real coordinate. On this basis, the method calculates the residual errors of two adjacent epochs, by which the residual errors of non-sampling epochs are fitted. Eventually, the virtual observation data are generated by the non-sampling epochs' residual errors, the distance between the satellite and the station and the estimated errors. Error characteristic of the virtual observation data is maintained, especially the common error between the base station and the rover station; This method only densifies the data of the base station, which will not affect the rover station. The experiment results show that when base station works with a sampling interval within 30 s, the virtual observation data generated by this method are accordant with the real data. In 30s, 15s and 5s sampling intervals, the standard errors of virtual observations of pseudo-range are about 0.2, 0.1 and 0.05 meters, and virtual observations of carrier phase are about 1.2, 0.7 and 0.2 cycles, respectively; In the case of 30 s sampling interval, the positioning result obtained by this method can still meet the cm level accuracy requirements.