## 3 Experimental Results
### Comparison of Location Results in Different Scenarios
#### 3.1.4 Headspace Occluded Scene  
The positioning result after setting the azimuth angle and mask angle of the headspace occluded scene in Section 2.2 is shown in the figure below. Figure 14: DOP value of the Surrounding occluded scene. Figure 13: Position error of the Surrounding occluded scene. As can be seen from Figure 15, under the Headspace occluded scenario, the GNSS constellation is limited by the range of mask angle, so the GNSS/INS positioning error increases by one order of magnitude in the celestial direction. The LEO constellation geometry has an obvious advantage in this scene, so the positioning error is not affected. Due to the geometric configuration advantage of the LEO constellation, the positioning accuracy of the federated filter is higher than that of GNSS/INS in this scene. As can be seen from Figure 16, the DOP value of the GNSS constellation in the Headspace occlusion scenario is significantly higher than that of the LEO constellation. The results of velocity error of the Headspace occluded scene can be seen from Figure 17 in Appendix A. The velocity error of INS/GNSS positioning has obvious fluctuation, and the fluctuation of attitude error has exceeded the acceptable range. The attitude angle error increases by one order of magnitude which can be seen from Figure 18 in Appendix A. It can be seen from the positioning results in Section 3.1, the positioning errors and constellation DOP values are quite different in different scenarios. The positioning results of the federated filtering algorithm in FNR mode are compared in the following table. In Table 6, this subsection compares the positioning results of four scenes. Experiments show that GNSS/INS, LEO/INS, and 5G/INS alone have poor positioning accuracy in a certain scene. However, by using the federated filtering algorithm, the positioning accuracy can be stabilized. At the same time, by comparing the positioning results in different scenarios, it can be found that the convergence effect of the federated filtering positioning results after the introduction of LEO constellation and 5G signals is more stable and faster than GNSS/INS. Plus, the positioning continuity is better guaranteed. In particular, the LEO/INS positioning effect is better in the Headspace occlusion scene. Figure 15: Position error of the Headspace occluded scene. Comparison of Different Information Factor Allocation and Location Results of Different Modes for Federated Filtering  
After comparing the positioning accuracy of integrated navigation in different scenes according to the different performance of positioning accuracy of GNSS, LEO, and 5G signals, we conclude that different sensors can be set with different information factors. So, the next experimental comparison can be made to verify the influence of information factor allocation of federated filtering in the same mode on positioning accuracy. Therefore, in the Open scene, the experimental results of different information factors under the FZR mode of federated filtering are compared, as shown in the following figures. In Figure 17, the position error of different information factors is shown. The velocity error and attitude error can be seen from Figures 18 and 19 in Appendix A. The values of different information factors can be seen in the following table.