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Inexpensive Kinematic Attitude Determination from MEMS-Based Accelerometers and GPS-Derived Accelerations

2002· article· en· W2037457085 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNAVIGATION Journal of the Institute of Navigation · 2002
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsAccelerometerKinematicsGlobal Positioning SystemAzimuthTrajectoryControl theory (sociology)AccelerationGeodesyDifferentiatorComputer scienceReference frameSimulationFrame (networking)PhysicsMathematicsGeologyGeometryComputer visionClassical mechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT: This paper describes an inexpensive kinematic attitude determination technique that uses GPS and a triaxial accelerometer. By removing the GPS-derived accelerations from the specific forces sensed by the accelerometers, the gravity vector in body frame of the vehicle is determined. From this, roll and pitch can be calculated. Azimuth is provided by the GPS-measured trajectory. Details are given on the system components and configuration, and on the kinematic attitude determination algorithm. A test of the technique shows that the root-mean-square (RMS) error is less than 1 deg. An analysis of Taylor series finite-difference differentiators in the frequency domain is also given. From this analysis, it is shown that for second derivatives, direct differentiators are preferable to cascaded differentiators unless the suppression of high frequencies is desired.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.665

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 communication0.0000.001
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 head0.027
GPT teacher head0.242
Teacher spread0.215 · 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