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Record W1963732499 · doi:10.1109/mesa.2010.5552088

Dead reckoning and Kalman filter design for trajectory tracking of a quadrotor UAV

2010· article· en· W1963732499 on OpenAlexaff
Qing-Li Zhou, Youmin Zhang, Yaohong Qu, Camille‐Alain Rabbath

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsDefence Research and Development CanadaConcordia University
Fundersnot available
KeywordsTrajectoryTracking (education)Kalman filterControl theory (sociology)Dead reckoningComputer scienceExtended Kalman filterTracking systemComputer visionArtificial intelligenceControl (management)Global Positioning SystemPhysics

Abstract

fetched live from OpenAlex

The dynamic equations of a quadrotor unmanned aerial vehicle (UAV), namely Qball-X4, are analyzed and built, trajectory tracking control design based on an inner/outer loop control structure is proposed in this paper. The method of analytic geometry is used in dead-reckoning, discrete Kalman filter has been applied to trajectory tracking for improving the accuracy of estimated location. Integrated online waypoints pre-planning and trajectory tracking control have been carried out. Simulation results demonstrate the effectiveness of the proposed approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.267
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations23
Published2010
Admission routes1
Has abstractyes

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