MétaCan
Menu
Back to cohort
Record W2556237456 · doi:10.1109/taes.2016.140898

Coherent radar processing in sea clutter environments, part 2: adaptive normalised matched filter versus adaptive matched filter performance

2016· article· en· W2556237456 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

VenueIEEE Transactions on Aerospace and Electronic Systems · 2016
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsClutterConstant false alarm rateSpace-time adaptive processingMoving target indicationAdaptive filterFilter (signal processing)Matched filterRadarRadar horizonComputer scienceStationary target indicationRemote sensingDetectorRadar trackerArtificial intelligenceComputer visionContinuous-wave radarRadar imagingAlgorithmTelecommunicationsGeology

Abstract

fetched live from OpenAlex

The maritime surveillance performance of the adaptive normalised matched filter (ANMF) detector structure against real multichannel, medium grazing angle, radar sea clutter data processed via space-time adaptive processing (STAP) is assessed and shown to exhibit constant false alarm rate (CFAR) performance characteristics which diverge from predictions over a large segment of the endo-clutter spectrum. The non-CFAR behaviour is linked to the existence of a two-component clutter model composed of contributions from Bragg and fast scattering mechanisms.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

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)0.0000.000

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 head0.015
GPT teacher head0.200
Teacher spread0.184 · 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