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Record W2364254628

Clutter Simulation and Compress of ISAR Imaging in Targets on Sea Surface

2007· article· en· W2364254628 on OpenAlex
YU Wen-xian, Atr Key

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

VenueModern Radar · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsClutterConstant false alarm rateInverse synthetic aperture radarComputer scienceArtificial intelligenceComputer visionRadarRemote sensingRadar imagingGeologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

When an inverse synthetic radar(ISAR) imaging the targets on sea surface,sea clutter may decrease the imaging performance.In this paper,we analyze the clutter probability distribution function,and examine the temporal and spatial correlation properties of the clutter.A sea-clutter simulation method is presented,and an algorithm to reject the sea clutter in ISAR signals is proposed.In the simulation method,the K-distribution coherent clutter is generated by spherically invariant random process,followed by two linear transformations,the two dimensional clutter with the given temporal and spatial correlation is obtained.In order to improve the ISAR images quality in presence of sea clutter,we set an appropriate threshold in the ISAR images through the constant false alarm rate processing,hence the scatter signals are retained and sea clutter is rejected.Finally the simulation results demonstrate the effectiveness of the algorithm.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.247

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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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