├── README.md
└── materials
    └── loca_plot_150dpi.png


/README.md:
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 1 | # Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network
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 3 | Data and Code will be published after 01.07.2023.
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 5 | <hr>
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 7 | ## Online Trajectory Estimation with NLOS Exclusion using different ML/DL Models
 8 | <img src="materials/loca_plot_150dpi.png" alt="Full_Trajectory" title="Optional title">
 9 | Vehicle Localization in Urban Area in Düsseldorf with NLOS Exclusion. The GNSS Positioning-Velocity-Timing (PVT) solution is shown in blue. Figure a) presents the estimated trajectory without NLOS exclusion and the solution by fusing GNSS observations and LiDAR odometry in a tight coupling. Figures b) and c) illustrate the estimated trajectories with NLOS exclusion using different learning models.
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11 | In this experiment, we infer our proposed network alongside other ML/DL models to identify the NLOS observations. The models are pre-trained with datasets from the cites of Hong Kong and Aachen. We choose the raw data from another measurement campaign in the city of Düsseldorf, which presents data Out-Of-Distribution for the pre-trained models.
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13 | <hr>
14 | 


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/materials/loca_plot_150dpi.png:
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https://raw.githubusercontent.com/rwth-irt/DeepNLOSDetection/2187b9c9aaf25bab5d5ad7414641fe36d294b3b3/materials/loca_plot_150dpi.png


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