Investigating Galileo Signal Tracking Challenges in Smartphones
Notice bibliographique
Résumé
Since 2016, Android smartphones have allowed access to the raw GNSS data, leading to significant improvements in positioning accuracy. Modern devices now support dual-frequency GNSS and multiple satellite systems, making positioning more reliable. Despite significant efforts in GNSS smartphone positioning, including using the Galileo constellation, several important issues still need to be addressed. Galileo signals have more complex modulation schemes compared to GPS signals. Practical tests show that after a short period, Galileo measurements' status may change from 'TOW Decoded' to 'E1C 2nd Code' status, where TOW represents the GNSS time of week. The receiver can stay on the 'E1C 2nd Code' status for several minutes. Some Galileo-ready chips track the data component to decode the navigation message. Once the ephemerides and clock data are decoded, they switch to tracking the E1C (pilot component), resulting in the 'E1C 2nd Code' status and ambiguous pseudoranges. In the current Galileo tracking approach, some satellites remain in 'TOW Known' or 'TOW Decoded' status for over an hour, while others switch to 'E1C 2nd Code Lock', resulting in ambiguous pseudoranges. The algorithm used to determine the tracking status for each satellite remains unclear. The white paper published by the European GNSS Agency’s (GSA) recommends checking the Galileo tracking status and highly advises using Galileo measurements only when in the E1C 2nd Code status.In this research, we will show that a 4 ms jump is still observed in some datasets, even though the tracking status is E1C 2nd Code. This confirms that verifying the signal tracking status alone is insufficient, as 4 ms jumps in the data can still occur despite this check. During these "jump epochs," erroneous measurements can adversely affect positioning accuracy. To investigate this issue, data collected by the Xiaomi Mi8 and Google Pixel 8 Pro devices are used. The results indicate that these jumps vary between devices and over time. The results also show that these jumps still occur, even though the tracking status is E1C 2nd Code.This 4 ms jump has also been addressed by Galluzzo et al. (2018) during the 2018 IPIN conference. They proposed a straightforward method to correct the pseudorange by detecting jumps through the difference between two consecutive epochs. If the difference is around 4 ms, the subsequent pseudoranges are adjusted accordingly. Although the theory behind this method is straightforward and effective in many cases, it cannot detect all jumps, for example, when those satellites first appear or when the pseudorange is missing. In this research, we employ the Observation Minus Calcaulation (OMC) to solve this issue and find the undetected 4 ms jumps. Finally, we investigate the accuracy of the kinematic data from Xiaomi Mi8 and Google Pixel 8 Pro devices with and without corrections for the 4 ms jumps. The results showed performance improvement in terms of the root mean square (RMS) and the 50th percentile of the horizontal positioning error after applying the correction for the 4 ms jump in Galileo measurements.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».