## 5 Validation Results and Analysis  
### Air-Ground NLOS Identification Based on Ray Intersection Detection  
To validate the algorithm proposed in this paper, small octree terrains containing domed buildings and C-shaped buildings were built for simulation validation of air-ground swarm NLOS identification based on LOS intersection detection. Figure 10 shows static scene 1, which contains a domed building, a navigation satellite and two UAVs. In fact, the visibility range in the algorithm is variable and can be changed accordingly, to simplify the calculation, the search range of LOS intersection detection was set to 300 m, which is used to analyze visibility and the range of interactions between swarms. The visible distance of the unmanned vehicle was calculated according to the octree terrain model terrain data. The visible distance of the unmanned vehicle by 3D terrain in scenes 1 and 2 is shown in Figures 12 and 13, respectively. Figure 11: Scenario 2 visibility judgment simulation. Figure 10: Scenario 1 visibility judgment simulation. Pseudocolor radar plots are used in Figures 12 and 13 to represent the visible distance of the unmanned vehicle in all directions. The blue color indicating the visible distance in the plots fades as the distance from the terrain to the unmanned vehicle in different directions increases. The positions of the navigation satellites and air-ground members relative to the unmanned vehicle are labeled Figures 12 and 13, respectively. If the traditional pure angular visibility judgment method is used to separate NLOS signals by comparing the elevation angle and cover angle, the LOS signals of vehicle 2 in position 1 of scenes 1 and 2 will be separated incorrectly. However, if the air-ground visibility analysis method proposed in this paper is used to separate the NLOS signals, due  
an octree structure, which simulates a typical urban environment. The model has the characteristics of an urban canyon, which creates a challenging environment for GNSS for an unmanned swarm. Based on the broadcast ephemeris file, this paper simulated the GPS constellation for a total duration of 800 s, other constellations can also be added in the case of multi-constellation applications. The air-ground swarms are equipped with an inertial guidance system, a satellite navigation system, an ultrawideband ranging system and a visual goniometric system, of which the inertial guidance system has a gyroscopic constant drift of 0.1 degree/h, the satellite navigation system has a pseudo-range mean-squared error of 5 m, the ultrawideband ranging system has a pseudo-range mean-squared error of 1 m, and the visual goniometric system has a goniometric mean-squared error of 0.1 degrees. Unmanned vehicle UGV1 moves at a speed of 0.3 m per second, and it is set to be surrounded by two reference unmanned vehicles numbered 2 and 3 and three reference vehicles numbered 4, 5, and 6, which perform belief propagation cooperative navigation-assisted localization of UGV1 and improve the localization accuracy of UGV1 when there are fewer available GPS satellites or when it is covered by terrain. The initial positions and motion characteristics of the unmanned vehicle are shown in Figure 14. Figures 15-17 show the elevation angle, azimuth and relative distance of each air-ground swarm member relative to unmanned vehicle 1, respectively. In the simulation process, the change curve of the number of visible satellites is shown in Figure 18. The number of visible satellites for unmanned vehicle 1 sometimes is less than 4, and satellite navigation is limited. Figure 19 shows the number of visible members of unmanned vehicle 1 under the traditional pure-angle visibility judgment method and the visibility judgment method based on LOS conditions used in this paper.