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Record W2912724935 · doi:10.1177/1729881418825407

Position deceptive tracking controller and parameters analysis via error characteristics for unmanned aerial vehicle

2019· article· en· W2912724935 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Advanced Robotic Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsnot available
FundersUniversity of CalgaryU.S. Department of Homeland Security
KeywordsComputer scienceOffset (computer science)Position (finance)Controller (irrigation)Control theory (sociology)Position errorSpoofing attackTracking (education)Tracking errorPath (computing)Real-time computingArtificial intelligenceControl (management)Orientation (vector space)Mathematics

Abstract

fetched live from OpenAlex

Covert Global Navigation Satellite System spoofer, called position deceptive tracking controller for unmanned aerial vehicle, is studied via analyzing the error characteristics in this article. Specifically, the following topics are discussed: (1) design the position deceptive tracking controller to make unmanned aerial vehicle deviate from the original path and follow up the spoofed new path point by point, and (2) analyze the related parameters by exploring the characteristics of the initial estimated state errors. Simulation results show the designed controller can realize the position offset of unmanned aerial vehicle unknowingly. What’s more, it can eliminate the initial state errors by selecting appropriate parameters.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.239
Teacher spread0.231 · 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