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Record W4317651090 · doi:10.2514/6.2023-2621

Design and Implementation of a Low-Cost Local Beacon System for GPS-Denied Environments

2023· article· en· W4317651090 on OpenAlexaff
Aref Asgari, Philip Ferguson

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBeaconGNSS applicationsGlobal Positioning SystemComputer scienceReal-time computingGNSS augmentationSatellite systemElectric beaconSatellite navigationPosition (finance)Radio navigationTelecommunicationsAir navigationGPS signalsDead reckoningPositioning systemAssisted GPSEngineeringNode (physics)

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-2621.vid GNSS-denied navigation has been the topic of much research for decades. Interference and occlusion by the surrounding environment limit GNSS access in urban environments. Poor GNSS coverage in the Arctic is another reason to pursue local navigation systems independent on satellite coverage. Our solution is to develop specially designed beacon systems that transmit the beacons' position data to a vehicle over radio signals. The receiving vehicle then uses the signal strength to estimate its position relative to the beacons. This paper provides the design and implementation of a low-cost beacon system that provides position information required for navigation in GNSS-denied environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.011
GPT teacher head0.241
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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