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Improving the Prediction of GNSS Satellite Visibility in Urban Canyons Based on a Graph Transformer

Shaolong Zheng, Kungan Zeng, Zhenni Li, Qianming Wang, Kan Xie, Ming Liu, and Shengli Xie
NAVIGATION: Journal of the Institute of Navigation December 2024, 71 (4) navi.676; DOI: https://doi.org/10.33012/navi.676

Article Figures & Data

Figures

  • FIGURE 1
    FIGURE 1

    The proposed method uses a GTNN to predict satellite visibility. The measurements from the GNSS receiver are first constructed into a sky satellite graph, which is then fed into the GTNN to extract the environment representations and improve the prediction of satellite visibility.

  • FIGURE 2
    FIGURE 2

    Structure of the GCN (Kipf and Welling, 2016)

  • FIGURE 3
    FIGURE 3

    A simple graph and representation of the adjacency matrix (a) Nodes and edges in the graph (b) Adjacency matrix

  • FIGURE 4
    FIGURE 4

    Sky plots captured at two different epochs with the satellite visibility labeled from ground truth

    B denotes BDS satellites, G denotes GPS satellites, and A denotes GAL satellites.

  • FIGURE 5
    FIGURE 5

    Distribution of C/N0 and proportion of NLOS/LOS signals

  • FIGURE 6
    FIGURE 6

    Architecture of the proposed GTNN for improving satellite visibility prediction The satellite node vector is represented by four features: elevation angle, azimuth angle, C/N0, and pseudorange residual. Edge connnections between satellites in the sky satellite graph are established when satellites either have a close spherical distance as determined by a threshold or belong to the same constellation. The graph is provided as input for the model to predict the visibility (LOS or NLOS) of satellites in the sky. The entire architecture is composed of an embedding layer for projecting, five GAblocks with multihead attention aggregation, and two cross-layer concatenating branches to alleviate over-smoothing.

  • FIGURE 7
    FIGURE 7

    Red represents the central satellite node, the dotted circle represents the k-th neighborhood, and different colors correspond to different neighborhood satellite nodes. Nodes with a direct edge connection with the central node are called first neighborhood nodes. The first layer of the GTNN can only learn the representation of the first neighborhood, but when the number of layers is sufficient, the central node can aggregate to the features of the global nodes.

  • FIGURE 8
    FIGURE 8

    The process of collecting GNSS measurements and constructing training and testing data

  • FIGURE 9
    FIGURE 9

    Sky images for 10 collection locations

    Ci (i = 1, 2, 3, 4, 5) represent locations in urban canyons, and Oi (i = 1, 2, 3, 4, 5) represent locations under an overpass.

  • FIGURE 10
    FIGURE 10

    Performance comparison between the proposed method and existing multipath detection methods for the urban canyon scenario

  • FIGURE 11
    FIGURE 11

    Performance comparison between the proposed method and existing multipath detection methods for the overpass scenario

  • FIGURE 12
    FIGURE 12

    Performance comparison between the proposed method and existing multipath detection methods for the hybrid scenario

  • FIGURE 13
    FIGURE 13

    Connection and attention weights of a sky satellite graph in GNSS measurements (a) Neighborhood connectivity of satellite 29. (b) Neighborhood connectivity of satellite 12. (c) Attention weight of satellite 29 and adjacent satellite. (d) Attention weight of satellite 12 and adjacent satellite.

  • FIGURE 14
    FIGURE 14

    Accuracy of the proposed GTNN with different numbers of GAblocks in the three test sets

  • FIGURE 15
    FIGURE 15

    Accuracy of the proposed GTNN with a range of γ values in the three test sets

Tables

  • TABLE 1

    Number of NLOS/LOS Samples and Corresponding Graphs for the Collected GNSS Data

    LocationC1C2C3C4C5O1O2O3O4O5
    NLOS13,02514,66511,8987,52515,54511,87921,38118,86318,16711,921
    LOS12,5799,16614,2784,4894,5166,7739,7779,3229,46417,425
    Graphs1,8031,8281,8261,5771,7991,4211,7551,6631,7451,786

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Improving the Prediction of GNSS Satellite Visibility in Urban Canyons Based on a Graph Transformer
Shaolong Zheng, Kungan Zeng, Zhenni Li, Qianming Wang, Kan Xie, Ming Liu,, Shengli Xie
NAVIGATION: Journal of the Institute of Navigation Dec 2024, 71 (4) navi.676; DOI: 10.33012/navi.676
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