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Record W3130840916 · doi:10.3390/s21041383

A New Perspective on Low-Cost MEMS-Based AHRS Determination

2021· article· en· W3130840916 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

VenueSensors · 2021
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAttitude and heading reference systemAccelerometerInitializationKalman filterOrientation (vector space)Control theory (sociology)GyroscopeComputer scienceProcess (computing)EngineeringArtificial intelligenceMathematicsAerospace engineering

Abstract

fetched live from OpenAlex

Attitude and heading reference system (AHRS) is the term used to describe a rigid body's angular orientation in three-dimensional space. This paper describes an AHRS determination and control system developed for navigation systems by integrating gyroscopes, accelerometers, and magnetometers signals from low-cost MEMS-based sensors in a complementary adaptive Kalman filter. AHRS estimation based on the iterative Kalman filtering process is required to be initialized first. A new method for AHRS initialization is proposed to improve the accuracy of the initial attitude estimates. Attitude estimates derived from the initialization and iterative adaptive filtering processes are compared with the orientation obtained from a high-end reference system. The improvement in the accuracy of the initial orientation as significant as 45% is obtained from the proposed method as compared with other selected techniques. Additionally, the computational process is reduced by 96%.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.467

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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