Height-constrained uncombined PPP for enhanced pedestrian and vehicular positioning with an Android smartphone
Notice bibliographique
Résumé
Before 2016, the users had access only to the position-velocity-time (PVT) information from the GNSS chipsets, and the raw GNSS observations were not available. The GNSS module's positioning accuracy on smartphones typically ranged from 3 to 5 meters under favorable multipath conditions, but over 10 meters in challenging environments. This level of accuracy was not sufficient for some applications. Fortunately, in May 2016, during the "Google I/O" conference, Google announced that the raw GNSS measurements, i.e., the pseudorange, carrier-phase, Doppler shift and carrier-to-noise density ratio (C/N0) observations, would be accessible through the Android Nougat (version 7) operating systems. Google has officially released Android 7 (Nougat) on August 22, 2016, marking a breakthrough for the GNSS community. Since then, research has been conducted to develop new algorithms to improve GNSS positioning performance using these mass-market devices. In 2021 and 2022, the Android GPS team of Google hosted two Google smartphone decimeter challenges (GSDC), where various smartphone GNSS datasets of real vehicular applications were used to determine smartphone positioning accuracies. As has been revealed, meter-level accuracy is generally achieved by the leading participants, which is still not enough to enable smartphone precise positioning. This indicates an ongoing demand to enhance the positioning accuracy with smartphones.Different positioning algorithms, such as absolute or relative positioning methods can be applied to the smartphone observations as well. Precise point positioning (PPP) is a powerful method for conducting accurate real-time positioning using a single receiver. Research papers have reported PPP smartphone positioning accuracy ranging from decimeter to sub-meter accuracy, depending on different factors such as the environment and positioning mode (static and kinematic). Most studies have so far focused on utilizing the GNSS only observations obtained from the smartphone's API. However, incorporating additional information as constraints can enhance accuracy and overall stability (for example height information).The Android operating system incorporates a set of functions known as APIs, allowing the users to use the system's features. Each Android version has distinct types of APIs. Among these, the android.location API is dedicated to the location-related services, with the "Location" class being one of them. This class consists of parameters such as latitude, longitude, altitude, timestamp, accuracy, bearing and velocity. The "AltitudeMeters" from this class provides the height above the WGS84 ellipsoid in meters, serving as supplementary information in this research. Although the vertical positioning accuracy of GNSS is generally lower than the horizontal accuracy, utilizing recorded height from the smartphone GNSS chipset can still be beneficial. This incorporation increases the degree of freedom and strengthens the geometry of the receiver and satellites. In this study, we assess the effectiveness of the uncombined PPP model in the presence of height constraints. We will utilize both pedestrian walking and vehicular datasets collected by a dual-frequency Xiaomi Mi8 device to evaluate the effect of adding height constraint to PPP model. We expect an improvement on the root-mean-square (RMS) of horizontal positioning, the 50th percentile error, and the convergence time when employing the height constraints.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».