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Record W4251570802 · doi:10.1002/cta.467

CFAR detection of weak target in clutter using chaos synchronization

2007· article· en· W4251570802 on OpenAlexaff
Di He, Henry Leung

Bibliographic record

VenueInternational Journal of Circuit Theory and Applications · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicChaos control and synchronization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsClutterConstant false alarm rateComputer scienceSynchronization (alternating current)Detection theoryChaoticSIGNAL (programming language)AlgorithmNoise (video)RadarCHAOS (operating system)Autoregressive modelDetectorArtificial intelligencePattern recognition (psychology)MathematicsTelecommunicationsStatisticsImage (mathematics)

Abstract

fetched live from OpenAlex

Abstract In this paper, a novel constant false alarm rate (CFAR) approach for detecting weak targets in sea clutter spectrum based on chaos synchronization is proposed. The weak target signal is detected when the synchronization between two identical chaotic systems is realized, even if the target spectrum lies inside the clutter spectrum. The threshold for the proposed CFAR detection is derived theoretically. The proposed chaos‐synchronization‐based CFAR technique is shown to be able to enhance the detectability of the target when the signal‐to‐clutter ratio and signal‐to‐noise ratio are low. Numerical experiments based on real radar sea clutter data confirm the effectiveness of the proposed chaos‐synchronization‐based CFAR detection method. The performance is superior to those of the standard autoregressive estimation‐based and the cell‐averaging CFAR detectors. Copyright © 2007 John Wiley & Sons, Ltd.

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.

How this classification was reachedexpand

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.256
Teacher spread0.249 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2007
Admission routes1
Has abstractyes

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