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Record W4414184914 · doi:10.3390/engproc2025088075

Enhancing GNSS Robustness in Automotive Applications with Supercorrelation: Experimental Results in Urban Scenarios

2025· article· en· W4414184914 on OpenAlexaff
Javier G. García, J. Rossouw van der Merwe, Hery Mwenegoha, Paulo Esteves, Samir Benmendil, Eugene Coetzee, James P. Ellis, Henry Eriksson-Martin, Rose Grey, Chris Higgins, Dana Jamal, Suraksha Kokradi, Ongun Kurt, Ramsey Faragher, Mark Crockett

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsTrusted Positioning (Canada)
Fundersnot available
KeywordsMultipath propagationGNSS applicationsRobustness (evolution)Multipath interferenceSatellite systemInterference (communication)Precise Point PositioningGlobal Positioning SystemAutomotive industry

Abstract

fetched live from OpenAlex

Mitigating multipath interference is one of the biggest challenges in radio positioning. The Supercorrelation™ technology developed via Focal Point Positioning (FPP) suppresses multipath interference by performing long coherent integration while undergoing complex motion to isolate the Line-Of-Sight (LOS) signals from the unwanted multipath interference.This article presents live results with a Supercorrelating Global Navigation Satellite System (S-GNSS) Software-Defined Radio (SDR), demonstrating significantly suppressed multipath to regain position accuracy.

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: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.375

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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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
Published2025
Admission routes1
Has abstractyes

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