Article Figures & Data
Figures
- ALGORITHM 1
Pseudocode for BRAIM
- ALGORITHM 2
Pseudocode for FDE+BRAIM
- FIGURE 1
Example of how BRAIM works. Weights of particles within AL (green) are summarized
- ALGORITHM 3
Pseudocode for SFC+BRAIM
- FIGURE 2
Example of how SFC+BRAIM works. Weights of particles within AL and the road feature are summarized
- FIGURE 3
Dynamic node (i.e., user) equipped with GNSS receiver and WASP sensor
- FIGURE 4
Data collection area with anchor nodes set up around the improvised road
- FIGURE 5
The trajectory of the dynamic node (red) and the locations of the anchor nodes (white pins)
- FIGURE 6
The velocity of the dynamic node during the data collection, as seen in Gabela et al., 2019
- FIGURE 7
Integrity estimation for GPS+WASP data depending on the applied integrity monitoring algorithm
- FIGURE 8
Integrity estimation for GPS+WASP(GMM) data for road-level positioning depending on the applied algorithm
- FIGURE 9
Integrity availability assessment for GPS+WASP data set for road-level positioning depending on the applied integrity monitoring algorithm
- FIGURE 10
Integrity availability assessment for GPS+WASP(GMM) data for road-level positioning depending on the applied algorithm
Tables
Parameters/Component number 1 2 3 μ[m] 0.0111 -0.5085 0.0776 σ2[m2] 0.0176 0.5335 0.0171 ω 0.4321 0.0414 0.5265 Data Algorithm Avg HPE [m] Std [m] Max [m] G Ba 4.32 3.85 21.14 FDE+B 2.96 1.41 19.26 SFC+B map+ 1m 3.03 2.02 16.89 SFC+B 2.91 1.80 15.83 FDE+SFC+B map+ 1m 2.62 1.12 9.42 FDE+SFC+B 2.63 1.19 9.44 G+W Gaussian B 0.25 0.17 1.74 SFC+B map+lm 0.24 0.14 0.91 SFC+B 0.27 0.15 1.52 FDE+SFC+B map+ 1m 0.25 0.16 1.18 FDE+SFC+B 0.28 0.17 1.60 G+W GMM B 0.26 0.28 4.31 FDE+B 0.31 0.44 5.46 SFC+B map+ 1m 0.24 0.20 3.43 SFC+B 0.26 0.26 4.68 FDE+SFC+B map+ 1m 0.29 0.35 5.33 FDE+SFC+B 0.31 0.42 5.99 ↵a BRAIM (B).
Data Algorithm Med PMI Avg PMI Max PMI G Ba 2.59e-01 3.18e-01 0.92 FDE+B 2.53e-01 3.05e-01 0.71 SFC+B map+ 1m 1.26e-01 1.64e-01 0.76 SFC+B 1.09e-01 1.47e-01 0.68 FDE+SFC+B map+ 1m 1.23e-01 1.55e-01 0.56 FDE+SFC+B 1.06e-01 1.34e-01 0.58 G+W Gaussian B 3.31e-10 5.24e-05 8.26e-03 FDE+B 2.68e-10 2.81e-08 1.84e-06 SFC+B map+ 1m 5.71e-12 4.89e-06 1.22e-03 SFC+B 1.325e- 5.07e-06 4.49e-03 12 FDE+SFC+B map+ 1m 5.48e-12 9.06e-10 2.97e-07 FDE+SFC+B 1.326e- 1.86e-09 1.52e-06 12 G+W GMM B 6.97e-07 1.08e-03 0.15 FDE+B 9.67e-07 3.90e-03 0.98 SFC+B map+ 1m 5.81e-08 2.05e-04 0.10 SFC+B 2.94e-08 4.29e-04 0.37 FDE+SFC+B map+ 1m 9.68e-08 8.94e-04 0.20 FDE+SFC+B 3.39e-08 7.40e-04 0.21 ↵a BRAIM (B).
Data Algorithm Med PMI Avg PMI Max PMI G+W Gaussian Ba 4.08e-02 7.09e-02 0.78 FDE+B 3.94e-02 6.31e-02 0.65 SFC+B map+ 1m 4.03e-02 6.91e-02 0.73 SFC+B 2.95e-02 4.82e-02 0.70 FDE+SFC+B map+ 1m 3.96e-02 6.19e-02 0.28 FDE+SFC+B 2.86e-02 4.34e-02 0.9999998 G+W GMM B 5.40e-03 4.02e-02 0.999995 FDE+B 5.73e-03 4.48e-02 0.9993 SFC+B map+ 1m 4.71e-03 3.51e-02 0.99995 SFC+B 3.40e-03 2.57e-02 0.99 FDE+SFC+B map+ 1m 5.79e-03 4.03e-02 0.998 FDE+SFC+B 3.80e-03 3.41e-02 1.00 ↵a BRAIM (B).
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