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Record W2155225811 · doi:10.1109/host.1997.613528

No evidence of stable distributions in radar clutter

2002· article· en· W2155225811 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 institutionsTechnical University of Nova Scotia
Fundersnot available
KeywordsClutterRadarConstant false alarm rateNoise (video)K-distributionGaussian noiseStable distributionComputer scienceGaussianFalse alarmAmplitudeProbability distributionAlgorithmStatisticsStatistical physicsMathematicsArtificial intelligencePhysicsTelecommunicationsOptics

Abstract

fetched live from OpenAlex

The alpha-stable distribution is a theoretical model for impulsive noise that currently enjoys wide success. In this paper, we test its applicability to high resolution radars that are capable of resolving fine structure of the sea surface. The received sea clutter signal by such systems is not well modeled by a Gaussian process, and we expected that stable distributions may provide better description of noise statistics than the conventional non-Gaussian models such as the K-distribution. However, in the important for radar low probability of false alarm region, we found that the K-distribution fits better the sea-clutter amplitude statistics than the alpha-stable distribution. In the application considered, we explain this failure of the stable model based on the analytical stable noise modeling.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.539

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 head0.025
GPT teacher head0.220
Teacher spread0.195 · 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
Published2002
Admission routes1
Has abstractyes

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