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Record W2115681244 · doi:10.1049/ip-rsn:20040197

Non-cooperative target recognition in the frequency domain

2004· article· en· W2115681244 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.

Bibliographic record

VenueIEE Proceedings - Radar Sonar and Navigation · 2004
Typearticle
Languageen
FieldEngineering
TopicInfrared Target Detection Methodologies
Canadian institutionsDefence Research and Development CanadaDepartment of National Defence
Fundersnot available
KeywordsFrequency domainDomain (mathematical analysis)Range (aeronautics)Computer scienceIdentification (biology)Discrete frequency domainTask (project management)Automatic target recognitionArtificial intelligencePattern recognition (psychology)Speech recognitionComputer visionEngineeringMathematicsAerospace engineeringBiology

Abstract

fetched live from OpenAlex

Non-cooperative target recognition is investigated in the frequency domain using measured in-flight aircraft data. It is found that the frequency domain target signatures are distinct for different aircraft types and at different aspects. As a result, target identification in the frequency domain is just as viable as using conventional high-range resolution profiles in the range domain. In addition, there are many advantages of working in the frequency domain; many of the problems encountered in the range domain can be avoided. This leads to a much simpler task in performing target recognition.

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)0.001
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: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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