MétaCan
Menu
Retour à la cohorte
Enregistrement W2736506171

An Investigation on the Contributing Factors of Enhanced DME Ranging Errors

2015· article· en· W2736506171 sur OpenAlexaboutno aff
Wouter Pelgrum, K. Li

Notice bibliographique

RevueProceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015) · 2015
Typearticle
Langueen
DomaineEngineering
ThématiqueGNSS positioning and interference
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRangingGNSS applicationsComputer scienceGlobal Positioning SystemSynchronizingSpoofing attackReal-time computingElectronic engineeringEngineeringTelecommunicationsComputer networkTransmission (telecommunications)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

With the introduction of Performance-Based Navigation (PBN) in NextGen and SESAR there is a significant increase in the navigation performance requirements. GNSS has become the cornerstone of future aviation navigation, and through ADS-B also of aviation surveillance. However, GNSS vulnerability to interference and jamming necessitates an Alternate Positioning Navigation and Timing or APNT solution that can meet the PBN performance requirements. One of the candidate APNT architectures is the Distance Measuring Equipment (DME) system. However, the legacy DME/N system’s performance is unlikely to meet the stringent APNT performance criteria (e.g. RNP 0.3). Many enabling technologies are proposed to enhance the DME performance, which evolves the legacy DME/N to an enhanced DME (eDME) system. Some enabling technologies include: - Carrier phase tracking, providing precise displacement measurements. These displacement measurements can be used in a “carrier-smoothed-pulse” range and a “pulse-minus-carrier” fashion, analogous to GPS, to increase accuracy and integrity of the ranging solution [1,2]. - Beat signal broadcast, facilitating robust carrier phase tracking and data broadcast. Passive DME ranging with unlimited capacity is possible when the beat signals from multiple transponders are time-synchronized [3]. - Data broadcast, for example by phase shift keying (PSK) of the second pulse of the DME beat signal pulse pairs. Data broadcast can be used for, for example, station identification, ephemeris, health, and signal authentication [4]. The abovementioned three key elements of the eDME system were successfully implemented and validated during a 6-hour flight test campaign in November 2014 [6]. During this flight test, a software-defined-radio- (SDR-) based data recording system was used to record all the outgoing and incoming pulses for both the airborne and the ground equipment. The high sampling rate recordings (10 mega samples per second) are precisely time-tagged with 10-ns resolution. These high-fidelity recordings enable a detailed qualitative and quantitative investigation into DME and eDME errors. A similar but more extensive eDME flight test is planned in March 2015 and will be used to expand this paper. The uplink, downlink, and two-way range performances are evaluated by post-processing of the SDR recordings. As a baseline, the results are first compared with the measurements from a modern DME interrogator. Next, an assessment is made of the sensitivity of DME/N and eDME ranging performance to a variety of system parameters. The same RF data set is processed using different algorithms and processing settings, to provide a fair comparison under a variety of propagation environments. Some of the factors that will be addressed in this paper are: - Smoothing time constant. Smoothing is an effective method of reducing noise and multipath errors. The optimal choice of the smoothing time constant and its merit is related to the multipath frequency components, and to the availability of external aiding, for example from eDME carrier phase [1] - Multipath-induced biases. Multipath with a fading period that is long compared to the receiver smoothing time, or multipath that is very strong compared to the direct signal, can introduce biases in the range measurements. Such bias errors can become a dominant factor in the accuracy and integrity error budget. - Pulse waveform and receiver processing bandwidth. A carefully chosen specification-compliant waveform can provide additional benefits for multipath mitigation. However, the receiver processing bandwidth is a key element to achieving these benefits. The impact of these factors will be discussed in this paper using the data from multiple flight tests that cover various propagation environments. Recommendations are made based on the findings. References: [1] K. Li and W. Pelgrum, Enhanced DME Carrier Phase: Concepts, Implementation, and Flight-test Results, NAVIGATION, vol. 62, no. 3, Fall 2013. [2] K. Li and W. Pelgrum, “Robust DME Carrier Phase Tracking Under Flight Dynamics,” Proceedings of the 2013 International Technical Meeting of The Institute of Navigation, San Diego, CA, January 2013. [3] L. Eldredge et al., “Alternative Positioning, Navigation & Timing (PNT) Study,” International Civil Aviation Organization Navigation Systems Panel NSP), Working Group Meetings, Montreal, Canada, May 2010 [4] “TACAN/DME Digital Data Broadcast Design Plan”, ADA001403, EDMAC Associates, Inc., East Rochester, NY, September 1974 [5] A. Naab-Levy, K. Li, and W. Pelgrum, “DME/N Error Budget Allocation and DME-Next Proof-of-Concept Flight Test and Performance Evaluation,” Proceedings of the ION 2013 Pacific PNT Meeting, Honululu, HI, April 2013. [6] W.Pelgrum et al., “eDME on-air: design, implementation, and demonstration,” submitted to Proceedings of the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, CA, Januray 2015.

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)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,493

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,267
Écart entre enseignants0,237 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

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

En bref

Citations7
Publié2015
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueProceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015)Même sujetGNSS positioning and interferenceTravaux en français237 207