NovAtel CORRECT with Precise Point Positioning (PPP) for High Accuracy Kinematic Applications
Notice bibliographique
Résumé
Precision GNSS users are continuously pushing the technology for improved performance that includes better accuracy, operation in more challenging conditions, and the ability to keep working for extended periods of time. The availability of reliable, high quality position solutions to meet these ever increasing demands in variable applications is the heart of NovAtel CORRECT™. There are a large number of applications that benefit from globally available cm-level positioning and, pairing core GNSS technology with corrections delivered by data providers like TerraStar, enables cm-level GNSS positioning in harsher conditions for applications like precision agriculture and mobile mapping. NovAtel CORRECT™ uses both PPP and RTK technologies to provide users with globally or locally available centimeter-level positioning. RTK has been widely used for many years. It enables the rapid acquisition of centimeter-level positions that are suitable for virtually all sky-visible high-precision applications. However, the logistical challenges associated with deploying and using RTK have meant that many applications have not been able to reap the benefits of high-precision positioning. NovAtel CORRECT™ expands the availability of centimeter-level positioning into many of these previously un-served applications. It combines globally valid and distributed corrections with advanced PPP algorithms, and lies near the apex of high-precision positioning technologies. This paper will discuss the recent developments of NovAtel CORRECT™ using the latest TerraStar correction data. From a user’s perceptive, the most important new feature is immediate re-convergence, in which the receiver can recover from short signal outages back to a similar level of position error as before the outage. Under normal conditions, complete signal interruptions of at least 60 seconds can be recovered from. Depending on the ionosphere and other observing conditions, even longer interruptions can be tolerated. This ability to recover quickly from GNSS and correction signal outages translates into increased working time for the user which can reduce downtime and increase overall productivity in any application. The improved kinematic performance of NovAtel CORRECT™ with the latest TerraStar data is demonstrated in this paper using agricultural-application field test data collected in Brazil and Canada. In addition, the initial convergence and final accuracy improvements of the TerraStar service are discussed and demonstrated. The key algorithmic improvement in NovAtel CORRECT™ is the addition of PPP carrier-phase integer ambiguity resolution. Ambiguity resolution is necessary to unlock the full accuracy of carrier-phase positioning. Furthermore, it enables the aforementioned immediate re-convergence. In NovAtel CORRECT™, PPP ambiguity resolution is enabled by the phase bias corrections broadcast by TerraStar. Resolution is carried out in two steps: wide-lane and narrow-lane resolution. In the first step, dual-frequency wide-lane ambiguities are estimated from the Melbourne-Wubbena combination and the ambiguities fixed to integers using the bootstrapping method. In the second step, narrow-lane ambiguities are calculated based on float ionosphere-free ambiguities and fixed wide-lane ambiguities. Narrow-lane ambiguity resolution is done by employing the LAMBDA method and the ambiguities validated using the ratio test.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,045 |
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 ».