# Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology +
## 3 GNSS Multipath Mitigation
In those cases, there is still some consistency between days, and normally only one day at a maximum is different from the rest, so that retaining the median as the multipath value for the model usually solves the problem. Figure 5: Example of Precise Point Positioning (PPP) residuals from four consecutive days after matching using azimuth and elevation. The GPS time is referred to the first day (2016 DOY 319). The obtained multipath model is shown in black. The figure above is a zoomed-in excerpt from Figure 6, which shows the residuals and the multipath model for the entire observation time for this particular satellite, that is, the approximately 6.5 h the satellite has been with an elevation above 10\({}^{\circ}\). Evidently, the observations of low elevation are strongly affected by multipath and should not be used for precise positioning. Now let us focus on the impact on DD processing. In Figures 7-9 the difference between the initial L1 DD residuals obtained from the Bernese solution (blue) and the L1 DD multipath correction given by the proposed multipath mitigation method (green) are plotted (red) for three different DD combinations and days. In the three examples, the L1 DD multipath error is mitigated and the new observed minus computed value is lower than one centimetre on average. The three examples also show that the proposed method seems to work independently of the elevation of the satellites, and even in the presence of data gaps. Figure 6: PPP residuals from four consecutive days after matching using azimuth and elevation for the entire observation session. The GPS time is referred to the first day (2016 DOY 319). The obtained multipath model is shown in black. Figure 7: The result of L1 DD multipath mitigation for satellites G15 and G20 (2016 DOY322). DD L1-BRN and DD L1-Model denote the DD L1 residuals obtained by means of Bernese and the use of the Model, respectively. As it can be seen Figure 7, the range of the obtained differences is 5 mm on average. However, some values at around 20.6 h have an absolute value of about 2 cm with residuals peaking \(-\)5 cm. Even in that case, the influence of the multipath error is largely diminished in the new observed-minus-computed-term, and the overall impact on the distance obtained can still be considered unbiased. The example in Figure 8 shows higher residuals that reach up to 8 cm. In this case, the bandwidth of the differences is over 1 cm, and slightly higher if compared to the previous example. Again, the differences seem to be randomly distributed around zero and no impact is expected in the subsequent obtained distance. Figure 4:0 The result of L1 DD multipath mitigation for satellites G12 and G10 (2016 DOY319). DD L1-BRN and DD L1-Model denote the DD L1 residuals obtained by means of Bernese and the use of the Model, respectively. Figure 4:1 The result of L1 DD multipath mitigation for satellites G24 and G18 (2016 DOY321). DD L1-BRN and DD L1-Model denote the DD L1 residuals obtained by means of Bernese and the use of the Model, respectively. Finally, the example given in Figure 9 has been chosen because the differences between the initial L1 DD residuals and the multipath corrections seem to be not so well centred as other DD combinations. In this case, although the overall range of the differences is over 8 mm, it can be seen that it is wider and slightly biased when both satellites are at a low elevation.