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Multi-Magnetometer Based Perturbation Mitigation for Indoor Orientation Estimation

2011· article· en· W2058846534 on OpenAlex
Muhammad Haris Afzal, Valérie Renaudin, Gérard Lachapelle

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNAVIGATION Journal of the Institute of Navigation · 2011
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMagnetometerOrientation (vector space)Magnetic fieldComputer sciencePerturbation (astronomy)Earth's magnetic fieldRemote sensingEnvironmental sciencePhysicsGeographyMathematics

Abstract

fetched live from OpenAlex

ABSTRACT: Determining orientation with respect to a known reference plays an important role in almost all modes of navigation. As the sensors required for measuring magnetic field have found their way into portable navigation devices, researchers have started investigating their application to orientation estimation in different environments. Nevertheless, the success of these sensors for orientation estimation is conditioned by their capacity to sense Earth's magnetic field in environments full of magnetic anomalies like urban canyons and indoors. These artificial fields contaminate Earth's magnetic field measurements, making orientation estimation very difficult in heavily perturbed areas. To overcome the effect of magnetic anomalies, a perturbation mitigation technique is proposed that utilizes multiple magnetometers. This mitigation technique is then used for estimating Earth's magnetic field indoors thus providing users with better magnetic orientation estimates. Performance of the proposed mitigation technique is assessed for pedestrian navigation in a shopping mall.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.435
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.250
Teacher spread0.227 · 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