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Record W1988450567 · doi:10.1109/cdc.2011.6160733

Invariant Extended Kalman Filter design for a magnetometer-plus-GPS aided inertial navigation system

2011· article· en· W1988450567 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaElse Kröner-Fresenius-Stiftung
KeywordsExtended Kalman filterInvariant extended Kalman filterControl theory (sociology)Inertial navigation systemObserver (physics)Invariant (physics)Kalman filterAlpha beta filterInertial measurement unitComputer scienceGlobal Positioning SystemConstructiveNonlinear systemInertial frame of referenceControl engineeringMathematicsArtificial intelligenceEngineeringPhysicsMoving horizon estimation

Abstract

fetched live from OpenAlex

We introduce a magnetometer-plus-GPS aided inertial navigation system for a helicopter UAV. A nonlinear observer is required to estimate the navigation states, typically an Extended Kalman Filter (EKF). A novel approach is the invariant observer, a constructive design method applicable to systems possessing symmetries. We review the theory and design an invariant observer for our example. Using an invariant observer guarantees a simplified form of the nonlinear estimation error dynamics. These are stabilized using an adaptation of the Invariant EKF, a systematic approach to compute the gains of an invariant observer. The resulting design is successfully implemented and validated in experiment and shows an improvement in performance over a conventional EKF.

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: none
Teacher disagreement score0.703
Threshold uncertainty score0.707

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 head0.040
GPT teacher head0.226
Teacher spread0.187 · 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

Quick stats

Citations22
Published2011
Admission routes2
Has abstractyes

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