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Record W7100205833

UNIVERSITY OF CALGARY Design of Galileo L1F Receiver Tracking Loops

2005· article· en· W7100205833 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsRobustness (evolution)Galileo (satellite navigation)Global Positioning SystemSatellite navigationTracking systemTracking (education)GNSS augmentationPseudorangeSensitivity (control systems)
DOInot available

Abstract

fetched live from OpenAlex

The ever-increasing public demand for location and positioning services has generated a demand for higher performance Global Navigation Satellite Systems (GNSS). The Galileo L1F signal, part of the European contribution to future GNSS, was designed to respond to the above demand in terms of measurement accuracy, tracking robustness and tracking sensitivity. Since the Galileo L1F public signal structure is almost fully specified, this thesis proposes novel tracking loop designs to increase code and phase measurements performance for potential implementation in future receivers. Using detailed modeling of each error that affects phase tracking, the Galileo L1F signal is shown to result in increased phase tracking robustness under dynamics, providing at least a 5-dB higher sensitivity and delivering more accurate measurements than the GPS C/A signal thanks to the availability of a dataless channel. Further phase measurement accuracy can be achieved through two proposed techniques that combine pilot channel phase tracking with the less robust data channel tracking. These techniques still maintain the same high resistance level towards dynamics as pilot channel-only phase tracking,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.183
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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