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Record W2240147219 · doi:10.1109/taes.2015.130197

Improving GNSS carrier-phase tracking via oscillator g-sensitivity compensation

2015· article· en· W2240147219 on OpenAlexaff
Srinivas Bhaskar, James T. Curran, Gérard Lachapelle

Bibliographic record

VenueIEEE Transactions on Aerospace and Electronic Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGNSS applicationsSensitivity (control systems)Compensation (psychology)Phase noiseTracking (education)Radar trackerPhase (matter)Phase compensationComputer scienceLocal oscillatorElectronic engineeringControl theory (sociology)Global Positioning SystemPhysicsEngineeringTelecommunicationsRadar

Abstract

fetched live from OpenAlex

In this paper the influence of oscillator g sensitivity on carrier-phase tracking performance in a global navigation satellite-system receiver is examined. It is illustrated that the local oscillator can be a limiting factor in the tracking threshold when the receiver is subject to pedestrian or vehicular dynamics. A novel feed-forward compensation technique is presented, which draws upon the inertial measurement unit resources of the receiver to alleviate these detrimental oscillator-based effects. Performance evaluation considered live global navigation satellite-system signals, a range of consumer-grade oscillators, and both high-quality and Microelectromechanical systems (MEMS)-grade inertial measurement units, in both pedestrian and vehicular operating environments. The results show that the process-noise spectral density corresponding to the signal acceleration state of the Kalman-filter-based tracker can be reduced significantly with compensation. It is also shown that three-dimensional velocity error reduces from 0.17 to 0.06 m/s with compensation.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.877

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 head0.016
GPT teacher head0.234
Teacher spread0.217 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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