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Record W4406029107 · doi:10.1002/9781394155293.ch10

Case Study #2

2025· other· en· W4406029107 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicSatellite Image Processing and Photogrammetry
Canadian institutionsCarleton University
Fundersnot available
KeywordsInertial measurement unitGNSS applicationsSensor fusionKalman filterComputer scienceCoordinate systemComputer visionOrientation (vector space)MATLABInertial navigation systemSatellite systemGeographic coordinate conversionGlobal Positioning SystemArtificial intelligenceControl engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

This chapter examines aerial vehicles' 3D motion system model and focuses on the fusion of Global Positioning System, vision, and radar measurements. It details the derivation and implementation of 3D IMU (Inertial measurement unit)/GNSS(Global navigation satellite system) fusion equations using the extended Kalman filter, including system, linearized error, and measurement models, providing a practical guide with MATLAB examples. The concept of the minimal coordinate principle and 3D orientation modeling using minimum coordinate parameters are detailed. The chapter highlights the challenges of aerial navigation and the integration of various sensor data for robust performance.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.105
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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