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Record W2058339336 · doi:10.1049/ip-rsn:20050003

Fast versus slow scan radar operation for coherent small target detection in sea clutter

2005· article· en· W2058339336 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 · 2005
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsClutterDetectorStationary target indicationDwell timeRadarTime delay and integrationRadar detectionRadar imagingConstant false alarm rateMoving target indicationRadar horizonRemote sensingComputer scienceLow probability of intercept radarOpticsArtificial intelligenceContinuous-wave radarPhysicsComputer visionGeologyTelecommunications

Abstract

fetched live from OpenAlex

Small maritime surface targets can be difficult to distinguish from sea clutter in radar backscattered signals, but discrimination may be improved by using coherent detectors within the dwell time of a scanning radar. Non-coherent integration, coherent integration, the Kelly detector and the adaptive linear quadratic detector are considered. Target detectability may also be improved by combining the results of a single dwell across multiple scans. Overall target detection times of 2, 5 and 10 s are considered and the trade-off between within-scan dwell time and multiple scan processing gain is investigated. Analysis of high-range-resolution coherent X-band data of small boats reveals that faster scan rates with corresponding shorter dwell times provide improved target detection performance over slower scan rates and longer dwell times.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.867

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 head0.013
GPT teacher head0.221
Teacher spread0.208 · 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