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Record W3028223633 · doi:10.1109/tim.2020.2995281

Improving Accuracy and Robustness in HF-RFID-Based Indoor Positioning With Kalman Filtering and Tukey Smoothing

2020· article· en· W3028223633 on OpenAlex

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

VenueIEEE Transactions on Instrumentation and Measurement · 2020
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRobustness (evolution)Kalman filterComputer scienceSmoothingPositioning systemObservational errorMeasurement uncertaintyIndoor positioning systemReal-time computingComputer visionArtificial intelligenceAccelerometerEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

In this article, we present a scalable, robust, and accurate indoor positioning system that uses a passive high-frequency radio frequency identification (HF RFID)-based positioning measurement system combined with Tukey smoother and a linear Kalman filter to locate mobile objects with an average measurement error of less than 3.7 cm. The proposed system is implemented and tested with extensive experiments, and our results show that the proposed system outperforms similar existing systems in minimizing the average positioning error and has better robustness against noisy sensor readings caused by hardware malfunctions or external error sources.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.526
Threshold uncertainty score0.624

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 head0.021
GPT teacher head0.205
Teacher spread0.184 · 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