###### Abstract
Barral V utilizes machine learning techniques to analyze actual UWB measurements in different scenarios, effectively separating NLOS-propagated positioning signals in UWB systems [13]. To achieve high-precision satellite positioning in urban environments, suppress the NLOS effect and improve navigation accuracy, [PERSON] of University College London proposed comparing the relative angle between the user and the satellite with the terrain contour in 3D digital maps, i.e., analyzing the terrain cover angle to judge the satellite visibility and separate the NLOS information [14]. In order to address the NLOS problem of smartphone navigation and localization in urban environments, [PERSON] introduced GNSS civil L5-band signals with stronger multipath resistance and used 3D terrain modelling to determine the NLOS signals [15]. [PERSON] analyzed the localization performance of different filtering algorithms combined with 3D terrain modelling NLOS suppression techniques, and the experimental results showed that filtering algorithms have a greater impact on mobile localization than on static localization and that the 3D terrain modelling NLOS suppression method has a better improvement effect on localization accuracy in dense environments [16]. Although several collaborative localization methods were proposed, further improving the localization accuracy of swarm members in signal-blocking environments remains a challenge. Therefore, in this paper, we will explore and improve the existing collaborative navigation algorithms to achieve high-precision swarm navigation in complex environments. This paper proposes a novel NLOS recognition method based on air-ground visibility analysis to improve cooperative navigation and positioning in complex signal shielding environments. It enhances NLOS signal recognition and separation accuracy, thereby boosting overall navigation performance. It introduces a collaborative navigation scheme using relative visibility analysis in Section 2, details a visibility analysis method for NLOS signal separation based on LOS conditions intersection and 3D terrain modelling in Section 3, and presents a cooperative localization method with heterogeneous ranging and angular observations in Section 4. Sections 3 and 4 are part of the method section. Section 5 validates the effectiveness of these methods through simulation experiments, demonstrating significant improvements in navigation and localization accuracy, particularly in challenging terrain conditions, this section includes data and results. Sections 6 and 7 contain the discussion and conclusion. Unlike the traditional consideration of GNSS satellite visibility only, i.e., analyzing GNSS visibility based on the masking angle, this paper investigates visibility in the context of swarm collaboration. The contribution of this paper lies in the fact that we further consider the effect of visibility after the introduction of collaboration among swarm members, establish a collaborative navigation graph model for the air-ground swarm by linearizing the ranging and angular observation data among the group members, and solve the collaborative localization problem through belief propagation. The proposed NLOS identification method based on visible vector masking visibility analysis reduces the influence of NLOS relative measurements through the combination of octree terrain data structure and LOS/3D terrain modelling cross-detection method, which is helpful to the improvement of swarm localization in urban environments.