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Record W1495683824 · doi:10.1109/sam.2002.1191006

Small ship detection with high frequency radar using an adaptive ocean clutter pre-whitened subspace method

2003· article· en· W1495683824 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsRaytheon Technologies (Canada)University of Victoria
Fundersnot available
KeywordsClutterSubspace topologyComputer scienceRadarConstant false alarm rateRadar horizonRemote sensingContinuous-wave radarRadar trackerDoppler effectDoppler frequencyStationary target indicationArtificial intelligenceComputer visionGeologyRadar imagingTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

We propose a novel scheme for using high frequency ocean surveillance radar (HFOSR) to detect slow weak target echoes embedded in temporally correlated sea clutter having a continuous spectrum. General Doppler processing CFAR detection of ships in ocean surveillance radar is usually inhibited by the continuous high order sea clutter. Conventional subspace methods can be utilized to enhance the detection, but they deteriorate dramatically in the presence of correlated sea clutter. In our paper an adaptive sea clutter filtering is introduced which improves the threshold and accuracy of the subspace detection method. Both simulated and real ship targets are used to verify the effectiveness of our proposed method.

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: none
Teacher disagreement score0.352
Threshold uncertainty score0.855

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 head0.029
GPT teacher head0.229
Teacher spread0.199 · 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

Quick stats

Citations7
Published2003
Admission routes1
Has abstractyes

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