MétaCan
Menu
Back to cohort
Record W7100944418

GPS Augmentation with Pseudolites for Navigation in Constricted Waterways, Proceedings of The Institute of Navigation 1997 National Technical Meeting

2015· article· en· W7100944418 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal Positioning SystemPseudorangeSatelliteReliability (semiconductor)Dilution of precisionSatellite navigationSatellite constellationConstellation
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates the use of portable ground-based transmitters, or pseudolites (PLs) (pseudo-satellites), to augment the existing space-borne GPS satellite constellation, with emphasis on the marine environment. A simulation analysis is conducted to investigate the effect of increasing mask angle on GPS availability, accuracy, and reliability measures. Up to three PLs at various locations are then introduced in the simulation to illustrate the improvements in performance. Finally, results from a field test conducted with one PL on Lake Okanagan in British Columbia, Canada, are presented. As expected, the use of a shore-based PL increased the number of observations and improved the availability of GPS. With appropriate estimation of the multipath component between the PL and the reference receiver, it was found that the accuracy of the DGPS position solution for the PL-augmented configuration was consistently better than that of the unaugmented GPS constellation. The use of a PL also improved the fault detection capability. An intentionally induced error on the remote receiver’s pseudorange measurement to a satellite was detected after the error reached 7.75 m for the unaugmented GPS constellation and 4.75 m for the PL-augmented constellation. The resulting horizontal errors were 9.4 m and 3.7 m, respectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.180

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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 designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicGNSS positioning and interferenceFrench-language works237,207