Evidence map›Paper›PMID 39860898›Full record

ArticleSensors (Basel, Switzerland)2025

A Transformer Encoder Approach for Localization Reconstruction During GPS Outages from an IMU and GPS-Based Sensor.

Kévin Cédric Guyard, Jonathan Bertolaccini, Stéphane Montavon, Michel Deriaz

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kévin Cédric GuyardInformation Science Institute, GSEM/CUI, University of Geneva, 1227 Carouge, Switzerland.ORCID 0009-0003-8997-8370
Jonathan BertolacciniInformation Science Institute, GSEM/CUI, University of Geneva, 1227 Carouge, Switzerland.
Stéphane MontavonVeterinary Department of the Swiss Armed Force, 3003 Berne, Switzerland.ORCID 0000-0002-2511-4974
Michel DeriazHaute Ecole de Gestion Genève, HES-SO, 1227 Carouge, Switzerland.ORCID 0009-0009-3048-9891

Funding

Innosuisse 106.657 IP-ICT
6 · The paper itself

Abstract

Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily focusing on real-time solutions. However, for applications that do not require real-time localization, these methods remain suboptimal. This paper presents a novel Transformer-based bidirectional encoder approach to address, in postprocessing, the localization challenges during GPS weak signal phases or GPS outages. Our method predicts the velocity during periods of weak or lost GPS signals and calculates the position through bidirectional velocity integration. Additionally, it incorporates position interpolation to ensure smooth transitions between active GPS and GPS outage phases. Applied to a dataset tracking horse positions-which features velocities up to 10 times those of pedestrians and higher acceleration-our approach achieved an average trajectory error below 3 m, while maintaining stable relative distance errors regardless of the GPS outage duration.

Indexed as

bidirectional encoderdeep learninglocalization reconstruction during GPS outagestime seriesTransformer

Identifiers

PMID39860898
PMCPMC11769138

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.