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Enregistrement W7010478229

Initial test of the proposed medium frequency R-Mode navigation message in the Baltic

2024· other· en· W7010478229 sur OpenAlexaboutno aff

Notice bibliographique

Revueelib (German Aerospace Center) · 2024
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTransmission (telecommunications)NucleofectionDerogationReliability (semiconductor)Synchronism
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

R-Mode is a promising technology to support safe navigation at sea. It provides complementary navigation signals to Global Navigation Satellite Systems (GNSS) in the medium frequency (MF) and/or very high frequency (VHF) band with a maritime terrestrial system. Due to the significantly lower transmission frequency, the R-Mode signals are not affected by the same threats and natural error influences as GNSS. R-Mode is therefore an ideal data source for the multi-system shipborne radionavigation receiver specified by the International Maritime Organization (IMO) to improve reliability and availability of position, velocity and timing (PVT) information and to support navigation and shipboard functions. R-Mode Baltic is a testbed in the Baltic Sea region which provides MF R-Mode signals of up to eight transmitting sites distributed across Germany, Sweden, Denmark and Poland. Furthermore, several temporary VHF R-Mode transmitter installations exist in an extended region in Poland, Norway and Germany. These installations were used to design the R-Mode signals, to learn how to implement R-Mode into existing maritime radio infrastructure as well as to test and validate the developed equipment. The knowledge gathered through these activities was used to support the R-Mode standardization at the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA). Moreover, further R-Mode research and development activities are ongoing in South Korea, US, UK, Canada and Romania. In 2023 the MF R-Mode transmitting sites of the R-Mode Baltic testbed broadcasted the R-Mode signals by combining the 100 bits/s MSK modulated data stream of the maritime radiobeacons and two additional carriers which carry the R-Mode timing information. The additional information about timing errors of the transmitted signals and other static and dynamic data corrections of the transmitting site were not broadcasted. These data, which are fundamental to obtain receiver performance improvement, are usually provided as navigation data by the system service provider. Inspired by the Galileo standard for navigation data and by adapting it to the needs of the MF R-Mode system, we proposed an R-Mode navigation message which can be broadcasted together with the station legacy service, aimed at the provision of code differential corrections for GNSS (DGNSS). Like DGNSS the R-Mode navigation message will follow the RTCM 2 standard of the message design. We introduced a new message with ID 55. The information about the R-Mode transmitting station like identification and status will be sent most frequently in the header of message 55. Four submessages were defined for the static (e.g. coordinates) and dynamic (e.g. clock error and health status) transmitter information. Additionally, two submessages were created during the work at IALA on the R-Mode standard to cover supplementary information from a differential R-Mode service, which were first tested in South Korea. This work is an essential basis for a guideline on Medium Frequency signal structure and navigation message, which is currently being developed at IALA and which is planned to be finalized in 2024. In preparation of the work for the project ORMOBASS, which aims to setup first core operational R-Mode functionalities in the Baltic Sea region, we wanted to prepare the German MF R-Mode sites and make a first test of the navigation information that we plan to broadcast within the upcoming three years. For this purpose, the transmitting sites in Germany were modified to reduce sudden jumps in the phase of the broadcasted R-Mode signal. Furthermore, we established for some days an MF R-Mode reference station at the island Fehmarn which is few tens of km away from the center between the three German MF R-Mode transmitting sites in Groß Mohrdorf, Zeven and on Helgoland. In addition, the Danish R-Mode transmitter on Bornholm could be received. Considering that we were not able to generate and broadcast the R-Mode navigation message directly from the transmitting sites we followed the approach to use the measurements of the reference station to derive all the necessary information and generate the navigation information that was distributed over the internet. At the same time a vessel of the DLR was equipped with an MF R-Mode receiver which was developed over recent years by DLR. The receiver was able to retrieve the navigation information by exploiting standard communication network and process this information to provide a positioning solution to the vessel. Moreover, for the first time we were able to perform positioning without using a GNSS-stabilized rubidium clock. By using predicted Atmospheric and Ground Delay Factor (AGDF) correction maps and an azimuth dependent correction function, we achieved a 95% positioning accuracy of 37 m at daytime and 86 m at nighttime when the vessel was sailing up to 9 km away from the reference station. Despite some technical problems which reduced the expected positioning performance, the receiver advancement in the processing of MF R-Mode signals remains remarkable and the achieved performance is suitable to support coastal navigation with R-Mode as backup system for GNSS.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,771
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,005

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,304
Écart entre enseignants0,292 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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