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Record W2060470451 · doi:10.1049/ip-rsn:20000240

Aircraft identification from RCS measurement using an orthogonal transform

2000· article· en· W2060470451 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 · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsDepartment of National DefenceRoyal Military College of Canada
Fundersnot available
KeywordsIdentification (biology)AlgorithmComputer scienceDiscrete Fourier transform (general)Computational complexity theoryRadar cross-sectionFrequency domainDiscrete cosine transformRange (aeronautics)Domain (mathematical analysis)RadarFractional Fourier transformMathematicsFourier transformArtificial intelligenceEngineeringTelecommunicationsComputer vision

Abstract

fetched live from OpenAlex

A comparative study on target identification using the radar cross section (RCS) signature of an aircraft in both the frequency domain and the range domain is conducted. A maximum likelihood method is employed to perform the identification process. Generalised likelihood identification when the received RCS signal is attenuated by an unknown amount is also examined. Target identification could be quite computationally intensive since a large number of library reference signatures may have to be searched to declare an identification. The use of an orthogonal transform is proposed to reduce the computational requirement. It is found that the discrete cosine transform is very effective in compacting the RCS signature in the frequency domain, and the Haar transform is more efficient in the range domain. The application of orthogonal transforms can reduce the computational complexity by at least 50% while maintaining the same identification accuracy.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.766

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 communication0.0000.001
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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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