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Record W2182843207

Pseudolite augmented navigation for automotive application

2011· article· en· W2182843207 on OpenAlexaboutno aff
Jacek Rapiński, Michał Śmieja

Bibliographic record

VenueJournal of KONES Powertrain and Transport · 2011
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGNSS applicationsGlobal Positioning SystemComputer scienceArea navigationConstellationReal-time computingGeodetic datumNavigation systemSystems engineeringTelecommunicationsEngineeringGeographyMobile robot
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the idea of the Local Area Navigation Systems (LANS) for automotive application in combination with standard GNSS navigation systems. The navigation system is based on four components: the widely available GPS, pseudolites located in areas where GPS signal is obstructed (tunnels, parking lots, city canyons), GPRS communication and supervisory informatics system. In this kind of system, reliable positioning is crucial. This article covers basic aspects of GPS single point positioning commonly used in navigation applications. Some information about differential positioning (DGPS/RTK) is also provided. A strong pressure is put on the application of pseudolite, its design and possible usability. This article presents authors own pseudolite design and the idea of pseudolite application to increase road safety in areas where standard GNSS signals are not available. The pseudolite presented in this paper is in the stage of development and testing. It is a device designed at the University of Warmia and Mazury in Olsztyn in cooperation with Canadian Center for Geodetic Engineering, University of New Brunswick, Canada. Different approach to the navigation in harsh environment is to move the source of the navigation outside of the vehicle and place it inside of the obstructions. It is much more efficient way to use in vehicles standard GPS receivers augmented with signals from the pseudolites, when the satellite signals are unavailable.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.279

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 head0.023
GPT teacher head0.238
Teacher spread0.215 · 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 designTheoretical or conceptual
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
Published2011
Admission routes1
Has abstractyes

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